The document presents a reinforcement learning approach called multi-armed bandits for ordering relevance judgements in information retrieval evaluation. It models document adjudication as a reinforcement learning problem where systems being evaluated are the arms and relevance judgements are rewards. Different bandit algorithms are tested, including epsilon-greedy, UCB, Bayesian methods. Experiments show bandit methods like MM and MTF outperform baselines at finding relevant documents with fewer judgements. Non-stationary variants that react quickly to non-relevant judgements perform best. The approach provides an effective framework for interactive information retrieval evaluation.
2010 04-20 san diego bootcamp - drools planner - use casesGeoffrey De Smet
Geoffrey De Smet discusses nurse rostering and hospital bed planning using Drools Planner. He outlines implementing hard and soft constraints for employee shift rostering and patient admission scheduling. Calculating the number of possible solutions for scheduling 2750 patients into 310 beds over 84 nights is over 10^6851, vastly larger than the number of atoms in the observable universe.
A Multi-armed Bandit Approach to Online Spatial Task AssignmentUmair ul Hassan
https://www.insight-centre.org/content/multi-armed-bandit-approach-online-spatial-task-assignment
Presented at UIC 2014
Abstract
Spatial crowdsourcing uses workers for performing tasks that require travel to different locations in the physical world. This paper considers the online spatial task assignment problem. In this problem, spatial tasks arrive in an online manner and an appropriate worker must be assigned to each task. However, outcome of an assignment is stochastic since the worker can choose to accept or reject the task. Primary goal of the assignment algorithm is to maximize the number of successful assignments over all tasks. This presents an exploration-exploitation challenge; the algorithm must learn the task acceptance behavior of workers while selecting the best worker based on the previous learning. We address this challenge by defining a framework for online spatial task assignment based on the multi-armed bandit formalization of the problem. Furthermore, we adapt a contextual bandit algorithm to assign a worker based on the spatial features
of tasks and workers. The algorithm simultaneously adapts
the worker assignment strategy based on the observed task
acceptance behavior of workers. Finally, we present an evaluation
methodology based on a real world dataset, and evaluate the
performance of the proposed algorithm against the baseline
algorithms. The results demonstrate that the proposed algorithm
performs better in terms of the number of successful assignments.
This document describes an insurance claim segmentation solution that uses predictive models and business rules to assign a risk score to incoming claims and route them to the appropriate claims handling channel. Claims are segmented into low, medium, and high risk categories. Low risk claims are fast tracked for quick processing, while high risk claims are investigated further. The goal is to lower costs by increasing the proportion of fast tracked claims, and improving the profitability of the forensic investigation unit. Two approaches for ranking vehicle repair suppliers are also presented: a mark-up based method, and an empirical predictive analytics approach using actual past repair costs.
Combining Linear and Non Linear Modeling Techniques Salford Systems
This document discusses how EMB America and Salford Systems can leverage their combined strengths in predictive modeling for the insurance industry. EMB specializes in insurance predictive modeling applications using their EMBLEM tool, while Salford Systems provides tree-based modeling techniques. The document outlines case studies where they have used both EMBLEM and CART (Classification and Regression Trees) models to identify important factors, interactions, and segments in large insurance datasets. Combining the approaches helped reduce modeling time and improve predictive performance for applications like customer retention modeling.
How to use R in different professions: R for Car Insurance Product (Speaker: ...Zurich_R_User_Group
This document discusses different statistical modeling approaches for pricing motor third party liability insurance. It begins by introducing the theoretical framework for pricing risk premiums based on expected claim frequency and severity. It then describes moving from a technical tariff to a commercial tariff by adjusting for safety and loading rates. The rest of the document applies generalized linear models (GLM), generalized non-linear models (GNM), and generalized additive models (GAM) to an Australian private motor insurance dataset to model stochastic risk premiums. It compares the results of the different modeling approaches based on metrics like the mean commercial tariff, loss ratio, explained deviance, and number of risk coefficients.
The Robos Are Coming - How AI will revolutionize Insurance 0117Graham Clark
1) Artificial intelligence and intelligent systems will transform many industries including banking and insurance by automating large portions of jobs currently performed by humans.
2) New intelligent concepts like digital assistants, semantic analysis, computer vision, augmented reality, and robotic process automation will impact customer experiences, product offerings, and business functions within insurance.
3) Insurance companies will be able to offer more personalized products and services using intelligent systems, and may require policyholders to use internet-connected devices or home automation to reduce risks and provide more customized coverage.
The document discusses optimization techniques for online experiments known as multi-armed bandits. It introduces the multi-armed bandit problem of balancing exploration of new options with exploitation of existing knowledge. It then describes several techniques for solving this problem, including AB testing, epsilon greedy, and upper confidence bound approaches. The document provides examples of how these techniques work and notes that multi-armed bandit methods can provide more efficient learning than AB testing alone.
Multi-Armed Bandits: Intro, examples and tricksIlias Flaounas
In this talk Ilias will discuss some variations of the Multi-Armed Bandits (MABs), a less popular although important area of Machine Learning. MABs enable us to build adaptive systems capable of finding solutions for tasks based on the interactions with their environment. MABs solve a task by acquiring useful knowledge at every step of an iterative process while they balance the exploration-exploitation dilemma. They are used to tackle practical problems like selecting appropriate online ads and personalized content for presentation to users; assigning people to cohorts in controlled trials; supporting decision making and more. To solve these kinds of problems solutions need to be identified as fast as possible since accepting errors can be costly. Ilias will discuss some examples from industry and academia as well as some of the related work at Atlassian.
2010 04-20 san diego bootcamp - drools planner - use casesGeoffrey De Smet
Geoffrey De Smet discusses nurse rostering and hospital bed planning using Drools Planner. He outlines implementing hard and soft constraints for employee shift rostering and patient admission scheduling. Calculating the number of possible solutions for scheduling 2750 patients into 310 beds over 84 nights is over 10^6851, vastly larger than the number of atoms in the observable universe.
A Multi-armed Bandit Approach to Online Spatial Task AssignmentUmair ul Hassan
https://www.insight-centre.org/content/multi-armed-bandit-approach-online-spatial-task-assignment
Presented at UIC 2014
Abstract
Spatial crowdsourcing uses workers for performing tasks that require travel to different locations in the physical world. This paper considers the online spatial task assignment problem. In this problem, spatial tasks arrive in an online manner and an appropriate worker must be assigned to each task. However, outcome of an assignment is stochastic since the worker can choose to accept or reject the task. Primary goal of the assignment algorithm is to maximize the number of successful assignments over all tasks. This presents an exploration-exploitation challenge; the algorithm must learn the task acceptance behavior of workers while selecting the best worker based on the previous learning. We address this challenge by defining a framework for online spatial task assignment based on the multi-armed bandit formalization of the problem. Furthermore, we adapt a contextual bandit algorithm to assign a worker based on the spatial features
of tasks and workers. The algorithm simultaneously adapts
the worker assignment strategy based on the observed task
acceptance behavior of workers. Finally, we present an evaluation
methodology based on a real world dataset, and evaluate the
performance of the proposed algorithm against the baseline
algorithms. The results demonstrate that the proposed algorithm
performs better in terms of the number of successful assignments.
This document describes an insurance claim segmentation solution that uses predictive models and business rules to assign a risk score to incoming claims and route them to the appropriate claims handling channel. Claims are segmented into low, medium, and high risk categories. Low risk claims are fast tracked for quick processing, while high risk claims are investigated further. The goal is to lower costs by increasing the proportion of fast tracked claims, and improving the profitability of the forensic investigation unit. Two approaches for ranking vehicle repair suppliers are also presented: a mark-up based method, and an empirical predictive analytics approach using actual past repair costs.
Combining Linear and Non Linear Modeling Techniques Salford Systems
This document discusses how EMB America and Salford Systems can leverage their combined strengths in predictive modeling for the insurance industry. EMB specializes in insurance predictive modeling applications using their EMBLEM tool, while Salford Systems provides tree-based modeling techniques. The document outlines case studies where they have used both EMBLEM and CART (Classification and Regression Trees) models to identify important factors, interactions, and segments in large insurance datasets. Combining the approaches helped reduce modeling time and improve predictive performance for applications like customer retention modeling.
How to use R in different professions: R for Car Insurance Product (Speaker: ...Zurich_R_User_Group
This document discusses different statistical modeling approaches for pricing motor third party liability insurance. It begins by introducing the theoretical framework for pricing risk premiums based on expected claim frequency and severity. It then describes moving from a technical tariff to a commercial tariff by adjusting for safety and loading rates. The rest of the document applies generalized linear models (GLM), generalized non-linear models (GNM), and generalized additive models (GAM) to an Australian private motor insurance dataset to model stochastic risk premiums. It compares the results of the different modeling approaches based on metrics like the mean commercial tariff, loss ratio, explained deviance, and number of risk coefficients.
The Robos Are Coming - How AI will revolutionize Insurance 0117Graham Clark
1) Artificial intelligence and intelligent systems will transform many industries including banking and insurance by automating large portions of jobs currently performed by humans.
2) New intelligent concepts like digital assistants, semantic analysis, computer vision, augmented reality, and robotic process automation will impact customer experiences, product offerings, and business functions within insurance.
3) Insurance companies will be able to offer more personalized products and services using intelligent systems, and may require policyholders to use internet-connected devices or home automation to reduce risks and provide more customized coverage.
The document discusses optimization techniques for online experiments known as multi-armed bandits. It introduces the multi-armed bandit problem of balancing exploration of new options with exploitation of existing knowledge. It then describes several techniques for solving this problem, including AB testing, epsilon greedy, and upper confidence bound approaches. The document provides examples of how these techniques work and notes that multi-armed bandit methods can provide more efficient learning than AB testing alone.
Multi-Armed Bandits: Intro, examples and tricksIlias Flaounas
In this talk Ilias will discuss some variations of the Multi-Armed Bandits (MABs), a less popular although important area of Machine Learning. MABs enable us to build adaptive systems capable of finding solutions for tasks based on the interactions with their environment. MABs solve a task by acquiring useful knowledge at every step of an iterative process while they balance the exploration-exploitation dilemma. They are used to tackle practical problems like selecting appropriate online ads and personalized content for presentation to users; assigning people to cohorts in controlled trials; supporting decision making and more. To solve these kinds of problems solutions need to be identified as fast as possible since accepting errors can be costly. Ilias will discuss some examples from industry and academia as well as some of the related work at Atlassian.
This document discusses predictive modeling approaches for life insurance underwriting. It took a long time for predictive modeling to be applied to underwriting due to the conservative nature of life insurance and the time needed to see results. Now, more data and computing power are available. Approaches include replicating current underwriting decisions or directly modeling applicant mortality rates. Various data sources can be used, including internal, third party, and customer data. Issues in building the predictive model include how to develop and update the model over time. Companies must decide how to incorporate these approaches and start collecting relevant data.
Traditional randomized experiments allow us to determine the overall causal impact of a treatment program (e.g. marketing, medical, social, education, political). Uplift modeling (also known as true lift, net lift, incremental lift) takes a further step to identify individuals who are truly positively influenced by a treatment through data mining / machine learning. This technique allows us to identify the “persuadables” and thus optimize target selection in order to maximize treatment benefits. This important subfield of data mining/data science/business analytics has gained significant attention in areas such as personalized marketing, personalized medicine, and political election with plenty of publications and presentations appeared in recent years from both industry practitioners and academics.
In this workshop, I will introduce the concept of Uplift, review existing methods, contrast with the traditional approach, and introduce a new method that can be implemented with standard software. A method and metrics for model assessment will be recommended. Our discussion will include new approaches to handling a general situation where only observational data are available, i.e. without randomized experiments, using techniques from causal inference. Additionally, an integrated modeling approach for uplift and direct response (where it can be identified who actually responded, e.g., click-through or coupon scanning) will be discussed. Last but not least, extension to the multiple treatment situation with solutions to optimizing treatments at the individual level will also be discussed. While the talk is geared towards marketing applications (“personalized marketing”), the same methodologies can be readily applied in other fields such as insurance, medicine, education, political, and social programs. Examples from the retail and non-profit industries will be used to illustrate the methodologies.
With data analysis showing up in domains as varied as baseball, evidence-based medicine, predicting recidivism and child support lapses, judging wine quality, credit scoring, supermarket scanner data analysis, and “genius” recommendation engines, “business analytics” is part of the zeitgeist. This is a good moment for actuaries to remember that their discipline is arguably the first – and a quarter of a millennium old – example of business analytics at work. Today, the widespread availability of sophisticated open-source statistical computing and data visualization environments provides the actuarial profession with an unprecedented opportunity to deepen its expertise as well as broaden its horizons, living up to its potential as a profession of creative and flexible data scientists.
This session will include an overview of the R statistical computing environment as well as a sequence of brief case studies of actuarial analyses in R. Case studies will include examples from loss distribution analysis, ratemaking, loss reserving, and predictive modeling.
This document discusses using R to price different types of insurance contracts. It provides examples of pricing life insurance, personal lines insurance, and excess of loss reinsurance contracts. For each type of insurance, it shows how to model costs and losses in R, calculate key metrics like expected claims and capital requirements, and determine final premiums. Code used in the examples is provided in an appendix.
The document defines key terms related to insurance pricing such as rate, exposure unit, pure premium, and loading. It describes the objectives of insurance pricing from both regulatory and business perspectives. The types of rating discussed include judgment rating, class rating, merit rating, schedule rating, experience rating, and retrospective rating. Class rating and two methods for determining class rates, pure premium and loss ratio, are explained in detail with examples. Merit rating adjusts class rates based on individual risk characteristics and loss experience.
Analysis of variance (ANOVA) is a statistical technique used to compare the means of three or more groups. It compares the variance between groups with the variance within groups to determine if the population means are significantly different. The key assumptions of ANOVA are independence, normality, and homogeneity of variances. A one-way ANOVA involves one independent variable with multiple levels or groups, and compares the group means to the overall mean to calculate an F-ratio statistic. If the F-ratio exceeds a critical value, then the null hypothesis that the group means are equal can be rejected.
Main Task Submit the Following 1. Calculate the sample size.docxinfantsuk
Main Task: Submit the Following
1.
Calculate the sample size needed given these factors:
· one-tailed t-test with two independent groups of equal size
· small effect size (see Piasta, S.B., & Justice, L.M., 2010)
· alpha =.05
· beta = .2
· Assume that the result is a sample size beyond what you can obtain. Use the compromise function to compute alpha and beta for a sample half the size. Indicate the resulting alpha and beta. Present an argument that your study is worth doing with the smaller sample.
2.
· Calculate the sample size needed given these factors:
· ANOVA (fixed effects, omnibus, one-way)
· small effect size
· alpha =.05
· beta = .2
· 3 groups
· Assume that the result is a sample size beyond what you can obtain. Use the compromise function to compute alpha and beta for a sample approximately half the size. Give your rationale for your selected beta/alpha ratio. Indicate the resulting alpha and beta. Give an argument that your study is worth doing with the smaller sample.
3. In a few sentences, describe two designs that can address your research question. The designs must involve two different statistical analyses. For each design, specify and justify each of the four factors and calculate the estimated sample size youll need. Give reasons for any parameters you need to specify for G*Power.
Include peer-reviewed journal articles as needed to support your responses to Part I.
Support your paper with a minimum of 5 resources. In addition to these specified resources, other appropriate scholarly resources, including older articles, may be included.
Length: 5 pages not including title and reference pages
ExamB/ExamB.php
<?php
// get user file
$filename = $_REQUEST['filepath'] ;
$validate = true ;
$x = array();
$y = array();
// var to get Point variable
$X_avg = $X_sum = 0 ;
$Y_avg = $Y_sum = 0 ;
if (!file_exists($filename)){
echo "Please correct file path." ;
}
else
// >>>>>>>>>>>>>>>>>> HERER <<<<<<<<<<<<<<<<<<<< //
{
// load code file
$Points = file_get_contents($filename) ;
// get code lines
$Points_lines = explode("\n", $Points);
// validate empty line
foreach ( $Points_lines as $line)
{
if(strlen($line) == 0 )
{
$validate = false ;
$validate_message = "Empty Line" ;
}
}
// validate pairs & Numbers
if($validate)
foreach ( $Points_lines as $line)
{
$Pairs = explode(",", $line);
if(strlen($Pairs[0]) == 0 || strlen($Pairs[1]) == 0 )
{
$validate = false ;
$validate_message = "Pairs Mismatching " ;
break;
}
else
{
if(is_numeric($Pairs[0]) && is_numeric($Pairs[1]) )
{
$validate = true ;
}
else
{
$validate = false ;
$validate_message = "Only numeric accepted" ;
break ;
}
}
}
// validate >= 0
if($validate)
foreach ( $Points_lines as $line)
{
$Pairs = explode(",", $line);
if(($Pairs[0] > 0 ) && ($Pairs[1] ...
ch_5 Game playing Min max and Alpha Beta pruning.pptSanGeet25
Game-Playing & Adversarial Search was covered in two lectures. Minimax search finds the optimal strategy but is impractical for large games. Minimax with alpha-beta pruning improves search efficiency by pruning subtrees that cannot affect the result. Iterative deepening allows more search within time limits by incrementally increasing search depth. Heuristics help guide search and handle limited lookahead.
Okay, here are the steps to convert each score to a z-score:
For history test:
Z = (X - Mean) / Standard Deviation
Z = (78 - 79) / 6
Z = -0.167
For math test:
Z = (X - Mean) / Standard Deviation
Z = (82 - 84) / 5
Z = 0.8
So the z-score for the history test is -0.167 and the z-score for the math test is 0.8.
Probability is a numerical measure of how likely an event is to occur. It is used in business to quantify uncertainty and make predictions. Some common applications in business include predicting sales based on price changes, estimating increases in productivity from new methods, and assessing the likelihood of investments being profitable. Probability is calculated on a scale of 0 to 1, with values closer to 1 indicating an event is more certain or likely to occur.
This document describes genetic algorithms and provides an example of how one works. It defines genetic algorithms as evolutionary algorithms that use techniques inspired by evolutionary biology like inheritance, mutation, selection, and crossover. The document then outlines the typical components of a genetic algorithm, including initialization of a random population, fitness evaluation, selection of parents, crossover and mutation to produce offspring, and iteration until a termination condition is met. It concludes by showing pseudocode for a genetic algorithm to solve the onemax problem and output from running the algorithm.
The document contains statistics lab report scores for 8 students who spent varying amounts of time preparing. It includes the regression equation relating hours spent to score and predicts a score for someone who spent 1 hour. It also defines the correlation coefficient and explains it measures the strength of the linear relationship between two variables.
The document provides guidance on using statistical functions on the TI-83/84 calculator. It discusses how to input data into lists, calculate descriptive statistics, create graphs, and perform probability, confidence interval, and hypothesis tests. For descriptive statistics, the user selects STAT > CALC > 1-Var Stats and inputs the appropriate data list. Graphs are made by selecting 2nd STAT PLOT and choosing the desired plot type and lists. Probability, interval, and hypothesis tests are accessed through the TESTS and TESTS menus and require selecting the appropriate function and inputting parameters like sample sizes, means, and standard deviations.
Computational Biology, Part 4 Protein Coding Regionsbutest
The document discusses different machine learning approaches for supervised classification and sequence analysis. It describes several classification algorithms like k-nearest neighbors, decision trees, linear discriminants, and support vector machines. It also discusses evaluating classifiers using cross-validation and confusion matrices. For sequence analysis, it covers using position-specific scoring matrices, hidden Markov models, cobbling, and family pairwise search to identify new members of protein families. It compares the performance of these different machine learning methods on sequence analysis tasks.
In this tutorial, we will learn the the following topics -
+ Voting Classifiers
+ Bagging and Pasting
+ Random Patches and Random Subspaces
+ Random Forests
+ Boosting
+ Stacking
This document summarizes four parallel search algorithms - Shared Transposition Tables, Root Splitting, Young Brothers Wait (YBW), and Dynamic Tree Splitting (DTS) - that were implemented in the computer chess program Prophet to parallelize alpha-beta search trees on symmetric multiprocessor machines. For each algorithm, the document describes the algorithm, its implementation in Prophet, and empirical performance results from Prophet. It concludes by discussing potential future work, including ways to improve split point selection for DTS and explore parallelization beyond SMP architectures.
1) The document provides instructions for students to conduct a coin toss experiment to demonstrate probability and use a chi-square test to analyze the results. It explains that genetics involves random chance processes that can be modeled with coin tosses.
2) Students will toss two coins 100 times in groups and record head/head, head/tail, and tail/tail outcomes. They will then use a chi-square test to compare observed results to expected results based on probability laws.
3) The chi-square test allows students to determine if any differences between observed and expected results are statistically significant, which could mean factors other than chance are influencing the outcomes. This analysis method is important for studying inheritance patterns in genetics.
This document discusses FASTA and BLAST algorithms for database searching to find similar sequences to a query. It explains that FASTA uses a "hit and extend" method to search for short identical matches, while BLAST searches for words above a threshold score rather than exact matches. BLAST is generally faster than FASTA and Smith-Waterman as it uses heuristics. The document provides details on how BLAST works including compiling a word list, searching the database for hits, and extending hits into alignments.
This document discusses predictive modeling approaches for life insurance underwriting. It took a long time for predictive modeling to be applied to underwriting due to the conservative nature of life insurance and the time needed to see results. Now, more data and computing power are available. Approaches include replicating current underwriting decisions or directly modeling applicant mortality rates. Various data sources can be used, including internal, third party, and customer data. Issues in building the predictive model include how to develop and update the model over time. Companies must decide how to incorporate these approaches and start collecting relevant data.
Traditional randomized experiments allow us to determine the overall causal impact of a treatment program (e.g. marketing, medical, social, education, political). Uplift modeling (also known as true lift, net lift, incremental lift) takes a further step to identify individuals who are truly positively influenced by a treatment through data mining / machine learning. This technique allows us to identify the “persuadables” and thus optimize target selection in order to maximize treatment benefits. This important subfield of data mining/data science/business analytics has gained significant attention in areas such as personalized marketing, personalized medicine, and political election with plenty of publications and presentations appeared in recent years from both industry practitioners and academics.
In this workshop, I will introduce the concept of Uplift, review existing methods, contrast with the traditional approach, and introduce a new method that can be implemented with standard software. A method and metrics for model assessment will be recommended. Our discussion will include new approaches to handling a general situation where only observational data are available, i.e. without randomized experiments, using techniques from causal inference. Additionally, an integrated modeling approach for uplift and direct response (where it can be identified who actually responded, e.g., click-through or coupon scanning) will be discussed. Last but not least, extension to the multiple treatment situation with solutions to optimizing treatments at the individual level will also be discussed. While the talk is geared towards marketing applications (“personalized marketing”), the same methodologies can be readily applied in other fields such as insurance, medicine, education, political, and social programs. Examples from the retail and non-profit industries will be used to illustrate the methodologies.
With data analysis showing up in domains as varied as baseball, evidence-based medicine, predicting recidivism and child support lapses, judging wine quality, credit scoring, supermarket scanner data analysis, and “genius” recommendation engines, “business analytics” is part of the zeitgeist. This is a good moment for actuaries to remember that their discipline is arguably the first – and a quarter of a millennium old – example of business analytics at work. Today, the widespread availability of sophisticated open-source statistical computing and data visualization environments provides the actuarial profession with an unprecedented opportunity to deepen its expertise as well as broaden its horizons, living up to its potential as a profession of creative and flexible data scientists.
This session will include an overview of the R statistical computing environment as well as a sequence of brief case studies of actuarial analyses in R. Case studies will include examples from loss distribution analysis, ratemaking, loss reserving, and predictive modeling.
This document discusses using R to price different types of insurance contracts. It provides examples of pricing life insurance, personal lines insurance, and excess of loss reinsurance contracts. For each type of insurance, it shows how to model costs and losses in R, calculate key metrics like expected claims and capital requirements, and determine final premiums. Code used in the examples is provided in an appendix.
The document defines key terms related to insurance pricing such as rate, exposure unit, pure premium, and loading. It describes the objectives of insurance pricing from both regulatory and business perspectives. The types of rating discussed include judgment rating, class rating, merit rating, schedule rating, experience rating, and retrospective rating. Class rating and two methods for determining class rates, pure premium and loss ratio, are explained in detail with examples. Merit rating adjusts class rates based on individual risk characteristics and loss experience.
Analysis of variance (ANOVA) is a statistical technique used to compare the means of three or more groups. It compares the variance between groups with the variance within groups to determine if the population means are significantly different. The key assumptions of ANOVA are independence, normality, and homogeneity of variances. A one-way ANOVA involves one independent variable with multiple levels or groups, and compares the group means to the overall mean to calculate an F-ratio statistic. If the F-ratio exceeds a critical value, then the null hypothesis that the group means are equal can be rejected.
Main Task Submit the Following 1. Calculate the sample size.docxinfantsuk
Main Task: Submit the Following
1.
Calculate the sample size needed given these factors:
· one-tailed t-test with two independent groups of equal size
· small effect size (see Piasta, S.B., & Justice, L.M., 2010)
· alpha =.05
· beta = .2
· Assume that the result is a sample size beyond what you can obtain. Use the compromise function to compute alpha and beta for a sample half the size. Indicate the resulting alpha and beta. Present an argument that your study is worth doing with the smaller sample.
2.
· Calculate the sample size needed given these factors:
· ANOVA (fixed effects, omnibus, one-way)
· small effect size
· alpha =.05
· beta = .2
· 3 groups
· Assume that the result is a sample size beyond what you can obtain. Use the compromise function to compute alpha and beta for a sample approximately half the size. Give your rationale for your selected beta/alpha ratio. Indicate the resulting alpha and beta. Give an argument that your study is worth doing with the smaller sample.
3. In a few sentences, describe two designs that can address your research question. The designs must involve two different statistical analyses. For each design, specify and justify each of the four factors and calculate the estimated sample size youll need. Give reasons for any parameters you need to specify for G*Power.
Include peer-reviewed journal articles as needed to support your responses to Part I.
Support your paper with a minimum of 5 resources. In addition to these specified resources, other appropriate scholarly resources, including older articles, may be included.
Length: 5 pages not including title and reference pages
ExamB/ExamB.php
<?php
// get user file
$filename = $_REQUEST['filepath'] ;
$validate = true ;
$x = array();
$y = array();
// var to get Point variable
$X_avg = $X_sum = 0 ;
$Y_avg = $Y_sum = 0 ;
if (!file_exists($filename)){
echo "Please correct file path." ;
}
else
// >>>>>>>>>>>>>>>>>> HERER <<<<<<<<<<<<<<<<<<<< //
{
// load code file
$Points = file_get_contents($filename) ;
// get code lines
$Points_lines = explode("\n", $Points);
// validate empty line
foreach ( $Points_lines as $line)
{
if(strlen($line) == 0 )
{
$validate = false ;
$validate_message = "Empty Line" ;
}
}
// validate pairs & Numbers
if($validate)
foreach ( $Points_lines as $line)
{
$Pairs = explode(",", $line);
if(strlen($Pairs[0]) == 0 || strlen($Pairs[1]) == 0 )
{
$validate = false ;
$validate_message = "Pairs Mismatching " ;
break;
}
else
{
if(is_numeric($Pairs[0]) && is_numeric($Pairs[1]) )
{
$validate = true ;
}
else
{
$validate = false ;
$validate_message = "Only numeric accepted" ;
break ;
}
}
}
// validate >= 0
if($validate)
foreach ( $Points_lines as $line)
{
$Pairs = explode(",", $line);
if(($Pairs[0] > 0 ) && ($Pairs[1] ...
ch_5 Game playing Min max and Alpha Beta pruning.pptSanGeet25
Game-Playing & Adversarial Search was covered in two lectures. Minimax search finds the optimal strategy but is impractical for large games. Minimax with alpha-beta pruning improves search efficiency by pruning subtrees that cannot affect the result. Iterative deepening allows more search within time limits by incrementally increasing search depth. Heuristics help guide search and handle limited lookahead.
Okay, here are the steps to convert each score to a z-score:
For history test:
Z = (X - Mean) / Standard Deviation
Z = (78 - 79) / 6
Z = -0.167
For math test:
Z = (X - Mean) / Standard Deviation
Z = (82 - 84) / 5
Z = 0.8
So the z-score for the history test is -0.167 and the z-score for the math test is 0.8.
Probability is a numerical measure of how likely an event is to occur. It is used in business to quantify uncertainty and make predictions. Some common applications in business include predicting sales based on price changes, estimating increases in productivity from new methods, and assessing the likelihood of investments being profitable. Probability is calculated on a scale of 0 to 1, with values closer to 1 indicating an event is more certain or likely to occur.
This document describes genetic algorithms and provides an example of how one works. It defines genetic algorithms as evolutionary algorithms that use techniques inspired by evolutionary biology like inheritance, mutation, selection, and crossover. The document then outlines the typical components of a genetic algorithm, including initialization of a random population, fitness evaluation, selection of parents, crossover and mutation to produce offspring, and iteration until a termination condition is met. It concludes by showing pseudocode for a genetic algorithm to solve the onemax problem and output from running the algorithm.
The document contains statistics lab report scores for 8 students who spent varying amounts of time preparing. It includes the regression equation relating hours spent to score and predicts a score for someone who spent 1 hour. It also defines the correlation coefficient and explains it measures the strength of the linear relationship between two variables.
The document provides guidance on using statistical functions on the TI-83/84 calculator. It discusses how to input data into lists, calculate descriptive statistics, create graphs, and perform probability, confidence interval, and hypothesis tests. For descriptive statistics, the user selects STAT > CALC > 1-Var Stats and inputs the appropriate data list. Graphs are made by selecting 2nd STAT PLOT and choosing the desired plot type and lists. Probability, interval, and hypothesis tests are accessed through the TESTS and TESTS menus and require selecting the appropriate function and inputting parameters like sample sizes, means, and standard deviations.
Computational Biology, Part 4 Protein Coding Regionsbutest
The document discusses different machine learning approaches for supervised classification and sequence analysis. It describes several classification algorithms like k-nearest neighbors, decision trees, linear discriminants, and support vector machines. It also discusses evaluating classifiers using cross-validation and confusion matrices. For sequence analysis, it covers using position-specific scoring matrices, hidden Markov models, cobbling, and family pairwise search to identify new members of protein families. It compares the performance of these different machine learning methods on sequence analysis tasks.
In this tutorial, we will learn the the following topics -
+ Voting Classifiers
+ Bagging and Pasting
+ Random Patches and Random Subspaces
+ Random Forests
+ Boosting
+ Stacking
This document summarizes four parallel search algorithms - Shared Transposition Tables, Root Splitting, Young Brothers Wait (YBW), and Dynamic Tree Splitting (DTS) - that were implemented in the computer chess program Prophet to parallelize alpha-beta search trees on symmetric multiprocessor machines. For each algorithm, the document describes the algorithm, its implementation in Prophet, and empirical performance results from Prophet. It concludes by discussing potential future work, including ways to improve split point selection for DTS and explore parallelization beyond SMP architectures.
1) The document provides instructions for students to conduct a coin toss experiment to demonstrate probability and use a chi-square test to analyze the results. It explains that genetics involves random chance processes that can be modeled with coin tosses.
2) Students will toss two coins 100 times in groups and record head/head, head/tail, and tail/tail outcomes. They will then use a chi-square test to compare observed results to expected results based on probability laws.
3) The chi-square test allows students to determine if any differences between observed and expected results are statistically significant, which could mean factors other than chance are influencing the outcomes. This analysis method is important for studying inheritance patterns in genetics.
This document discusses FASTA and BLAST algorithms for database searching to find similar sequences to a query. It explains that FASTA uses a "hit and extend" method to search for short identical matches, while BLAST searches for words above a threshold score rather than exact matches. BLAST is generally faster than FASTA and Smith-Waterman as it uses heuristics. The document provides details on how BLAST works including compiling a word list, searching the database for hits, and extending hits into alignments.
The document discusses different types of adversarial search algorithms. It describes min-max algorithm and alpha-beta pruning. Min-max algorithm searches through the game tree recursively to find the optimal move assuming the opponent plays optimally. Alpha-beta pruning improves on min-max by pruning parts of the tree that cannot contain better moves based on the alpha and beta values being passed down the tree.
This document describes how to perform a chi-square test to determine if two genes are independently assorting or linked. It explains that for a two-point testcross of a heterozygote individual, you expect a 25% ratio for each of the four possible offspring genotypes if the genes are independent. The chi-square test compares observed vs. expected offspring ratios. It notes that the standard test assumes equal segregation of alleles, which may not always be true.
Data Analytics Project_Eun Seuk Choi (Eric)Eric Choi
This document describes a linear regression analysis conducted to predict NBA players' wins contributed (WINS) using minutes played (M), games played (GP), offensive rating (ORPM), and defensive rating (DRPM). The final model was WINS~GP+M+ORPM+DRPM, which had an R^2 of 0.8575. Cross-validation showed the model predicted out-of-sample data well. The analysis found ORPM was most predictive of WINS based on its confidence interval not containing 0.
1. The document discusses simulations involving coin flips, basketball free throws, and randomly selecting students. It prompts the reader to identify the component, trial, response variable, and statistic for different simulation scenarios.
2. Examples of simulations include flipping a coin 100 times to determine if it is biased, shooting free throws until a miss to measure success rate, and randomly selecting 3 students from a class.
3. The reader is asked to consider how the simulations would change based on the probability of success, number of trials, and structure of dependent vs independent events.
Similar to Feeling Lucky? Multi-armed Bandits for Ordering Judgements in Pooling-based Evaluation (20)
In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject.
We will explore the capabilities of AI in understanding XML markup languages and autonomously creating structured XML content. Additionally, we will examine the capacity of AI to enrich plain text with appropriate XML markup. Practical examples and methodological guidelines will be provided to elucidate how AI can be effectively prompted to interpret and generate accurate XML markup.
Further emphasis will be placed on the role of AI in developing XSLT, or schemas such as XSD and Schematron. We will address the techniques and strategies adopted to create prompts for generating code, explaining code, or refactoring the code, and the results achieved.
The discussion will extend to how AI can be used to transform XML content. In particular, the focus will be on the use of AI XPath extension functions in XSLT, Schematron, Schematron Quick Fixes, or for XML content refactoring.
The presentation aims to deliver a comprehensive overview of AI usage in XML development, providing attendees with the necessary knowledge to make informed decisions. Whether you’re at the early stages of adopting AI or considering integrating it in advanced XML development, this presentation will cover all levels of expertise.
By highlighting the potential advantages and challenges of integrating AI with XML development tools and languages, the presentation seeks to inspire thoughtful conversation around the future of XML development. We’ll not only delve into the technical aspects of AI-powered XML development but also discuss practical implications and possible future directions.
“An Outlook of the Ongoing and Future Relationship between Blockchain Technologies and Process-aware Information Systems.” Invited talk at the joint workshop on Blockchain for Information Systems (BC4IS) and Blockchain for Trusted Data Sharing (B4TDS), co-located with with the 36th International Conference on Advanced Information Systems Engineering (CAiSE), 3 June 2024, Limassol, Cyprus.
20 Comprehensive Checklist of Designing and Developing a WebsitePixlogix Infotech
Dive into the world of Website Designing and Developing with Pixlogix! Looking to create a stunning online presence? Look no further! Our comprehensive checklist covers everything you need to know to craft a website that stands out. From user-friendly design to seamless functionality, we've got you covered. Don't miss out on this invaluable resource! Check out our checklist now at Pixlogix and start your journey towards a captivating online presence today.
In his public lecture, Christian Timmerer provides insights into the fascinating history of video streaming, starting from its humble beginnings before YouTube to the groundbreaking technologies that now dominate platforms like Netflix and ORF ON. Timmerer also presents provocative contributions of his own that have significantly influenced the industry. He concludes by looking at future challenges and invites the audience to join in a discussion.
Alt. GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using ...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
How to Get CNIC Information System with Paksim Ga.pptxdanishmna97
Pakdata Cf is a groundbreaking system designed to streamline and facilitate access to CNIC information. This innovative platform leverages advanced technology to provide users with efficient and secure access to their CNIC details.
UiPath Test Automation using UiPath Test Suite series, part 6DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 6. In this session, we will cover Test Automation with generative AI and Open AI.
UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities.
Throughout the session, participants will discover how this synergy empowers testers to automate repetitive tasks, enhance testing accuracy, and expedite the software testing life cycle. Topics covered include the seamless integration process, practical use cases, and the benefits of harnessing AI-driven automation for UiPath testing initiatives. By attending this webinar, testers, and automation professionals can gain valuable insights into harnessing the power of AI to optimize their test automation workflows within the UiPath ecosystem, ultimately driving efficiency and quality in software development processes.
What will you get from this session?
1. Insights into integrating generative AI.
2. Understanding how this integration enhances test automation within the UiPath platform
3. Practical demonstrations
4. Exploration of real-world use cases illustrating the benefits of AI-driven test automation for UiPath
Topics covered:
What is generative AI
Test Automation with generative AI and Open AI.
UiPath integration with generative AI
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Enchancing adoption of Open Source Libraries. A case study on Albumentations.AIVladimir Iglovikov, Ph.D.
Presented by Vladimir Iglovikov:
- https://www.linkedin.com/in/iglovikov/
- https://x.com/viglovikov
- https://www.instagram.com/ternaus/
This presentation delves into the journey of Albumentations.ai, a highly successful open-source library for data augmentation.
Created out of a necessity for superior performance in Kaggle competitions, Albumentations has grown to become a widely used tool among data scientists and machine learning practitioners.
This case study covers various aspects, including:
People: The contributors and community that have supported Albumentations.
Metrics: The success indicators such as downloads, daily active users, GitHub stars, and financial contributions.
Challenges: The hurdles in monetizing open-source projects and measuring user engagement.
Development Practices: Best practices for creating, maintaining, and scaling open-source libraries, including code hygiene, CI/CD, and fast iteration.
Community Building: Strategies for making adoption easy, iterating quickly, and fostering a vibrant, engaged community.
Marketing: Both online and offline marketing tactics, focusing on real, impactful interactions and collaborations.
Mental Health: Maintaining balance and not feeling pressured by user demands.
Key insights include the importance of automation, making the adoption process seamless, and leveraging offline interactions for marketing. The presentation also emphasizes the need for continuous small improvements and building a friendly, inclusive community that contributes to the project's growth.
Vladimir Iglovikov brings his extensive experience as a Kaggle Grandmaster, ex-Staff ML Engineer at Lyft, sharing valuable lessons and practical advice for anyone looking to enhance the adoption of their open-source projects.
Explore more about Albumentations and join the community at:
GitHub: https://github.com/albumentations-team/albumentations
Website: https://albumentations.ai/
LinkedIn: https://www.linkedin.com/company/100504475
Twitter: https://x.com/albumentations
Full-RAG: A modern architecture for hyper-personalizationZilliz
Mike Del Balso, CEO & Co-Founder at Tecton, presents "Full RAG," a novel approach to AI recommendation systems, aiming to push beyond the limitations of traditional models through a deep integration of contextual insights and real-time data, leveraging the Retrieval-Augmented Generation architecture. This talk will outline Full RAG's potential to significantly enhance personalization, address engineering challenges such as data management and model training, and introduce data enrichment with reranking as a key solution. Attendees will gain crucial insights into the importance of hyperpersonalization in AI, the capabilities of Full RAG for advanced personalization, and strategies for managing complex data integrations for deploying cutting-edge AI solutions.
Pushing the limits of ePRTC: 100ns holdover for 100 daysAdtran
At WSTS 2024, Alon Stern explored the topic of parametric holdover and explained how recent research findings can be implemented in real-world PNT networks to achieve 100 nanoseconds of accuracy for up to 100 days.
GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024Neo4j
Neha Bajwa, Vice President of Product Marketing, Neo4j
Join us as we explore breakthrough innovations enabled by interconnected data and AI. Discover firsthand how organizations use relationships in data to uncover contextual insights and solve our most pressing challenges – from optimizing supply chains, detecting fraud, and improving customer experiences to accelerating drug discoveries.
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slackshyamraj55
Discover the seamless integration of RPA (Robotic Process Automation), COMPOSER, and APM with AWS IDP enhanced with Slack notifications. Explore how these technologies converge to streamline workflows, optimize performance, and ensure secure access, all while leveraging the power of AWS IDP and real-time communication via Slack notifications.
Goodbye Windows 11: Make Way for Nitrux Linux 3.5.0!SOFTTECHHUB
As the digital landscape continually evolves, operating systems play a critical role in shaping user experiences and productivity. The launch of Nitrux Linux 3.5.0 marks a significant milestone, offering a robust alternative to traditional systems such as Windows 11. This article delves into the essence of Nitrux Linux 3.5.0, exploring its unique features, advantages, and how it stands as a compelling choice for both casual users and tech enthusiasts.
Goodbye Windows 11: Make Way for Nitrux Linux 3.5.0!
Feeling Lucky? Multi-armed Bandits for Ordering Judgements in Pooling-based Evaluation
1. Feeling Lucky? Multi-armed bandits for Ordering Judgements
in Pooling-based Evaluation
David E. Losada
Javier Parapar, Álvaro Barreiro
ACM SAC, 2016
11. relevance assessments are incomplete
101. AP555
.
.
.
101. AP555
.
.
.
101. AP555
.
.
.
101. AP555
.
.
.
...
pool
depth
rankings of docs by estimated relevance (runs)
12. relevance assessments are incomplete
101. AP555
.
.
.
101. AP555
.
.
.
101. AP555
.
.
.
101. AP555
.
.
.
...
pool
depth
rankings of docs by estimated relevance (runs)
WSJ13
WSJ17 AP567
WSJ19AP111 CR93E
ZF207AP881FT967
pool
...
13. relevance assessments are incomplete
101. AP555
.
.
.
101. AP555
.
.
.
101. AP555
.
.
.
101. AP555
.
.
.
...
pool
depth
rankings of docs by estimated relevance (runs)
WSJ13
WSJ17 AP567
WSJ19AP111 CR93E
ZF207AP881FT967
pool
...
human assessments
14. finding relevant docs is the key
Most productive use of assessors' time
is spent on judging relevant docs
(Sanderson & Zobel, 2005)
15. Effective adjudication methods
Give priority to pooled docs that are
potentially relevant
Can signifcantly reduce the num. of
judgements required to identify a given
num. of relevant docs
But most existing methods are adhoc...
16. Our main idea...
Cast doc adjudication as a
reinforcement learning problem
Doc judging is an iterative process where
we learn as judgements come in
17. Doc adjudication as a reinforcement learning problem
Initially we know nothing about the quality of the runs
? ? ? ?...
As judgements
come in...
And we can adapt and allocate more docs for judgement from
the most promising runs
25. exploration vs exploitation
exploits current knowledge
spends no time sampling inferior actions
maximizes expected reward
on the next action
explores uncertain actions
gets more info about expected payofs
may produce greater total reward
in the long run
allocation methods: choose next action (play) based on past plays and obtained rewards
implement diferent ways to trade between exploration and exploitation
26. Multi-armed bandits for ordering judgements
...
machines = runs
...
play a machine = select a run and get the next (unjudged) doc
1. WSJ13
2. CR93E
.
.
(binary) reward = relevance/non-relevance of the selected doc
27. Allocation methods tested
...
random ϵn
-greedy
with prob 1-ϵ plays the machine
with the highest avg reward
with prob ϵ plays a
random machine
prob of exploration (ϵ) decreases
with the num. of plays
Upper Confdence Bound
(UCB)
computes upper confdence
bounds for avg rewards
conf. intervals get narrower
with the number of plays
selects the machine with the
highest optimistic estimate
28. Allocation methods tested: Bayesian bandits
prior probabilities of giving a relevant doc: Uniform(0,1) ( or, equivalently, Beta(α,β), α,β=1 )
U(0,1) U(0,1) U(0,1) U(0,1)
...
evidence (O ∈ {0,1}) is Bernoulli (or, equivalently, Binomial(1,p) )
posterior probabilities of giving a relevant doc: Beta(α+O, β+1-O) (Beta: conjugate prior
for Binomial)
36. Allocation methods tested: Bayesian bandits
...
we iteratively update our estimations using Bayes:
two strategies to select the next machine:
Bayesian Learning Automaton (BLA): draws a sample from each the posterior distribution
and selects the machine yieding the highest sample
MaxMean (MM): selects the machine with the highest expectation of the posterior distribution
37. test different document adjudication strategies in
terms of how quickly they find the relevant
docs in the pool
experiments
# rel docs found at diff. number of
judgements performed
41. experiments: baselines
1. WSJ13
2. WSJ17
3. AP567
.
.
...
1. FT941
2. WSJ13
3. WSJ19
.
.
1. WSJ13
2. CR93E
3. AP111
.
.
MoveToFront (MTF) (Cormack et al 98)
starts with uniform priorities for all runs (e.g. max priority=100)
selects a random run (from those with max priority)
1. ZF207
2. AP881
3. FT967
.
.
100 100 100 100
42. experiments: baselines
1. WSJ13
2. WSJ17
3. AP567
.
.
...
1. FT941
2. WSJ13
3. WSJ19
.
.
1. WSJ13
2. CR93E
3. AP111
.
.
MoveToFront (MTF) (Cormack et al 98)
starts with uniform priorities for all runs (e.g. max priority=100)
selects a random run (from those with max priority)
1. ZF207
2. AP881
3. FT967
.
.
100 100 100 100
43. experiments: baselines
1. WSJ13
2. CR93E
3. AP111
.
.
MoveToFront (MTF) (Cormack et al 98)
extracts & judges docs from the selected run
stays in the run until a non-rel doc is found
100
44. experiments: baselines
1. WSJ13
2. CR93E
3. AP111
.
.
MoveToFront (MTF) (Cormack et al 98)
extracts & judges docs from the selected run
stays in the run until a non-rel doc is found
100
WSJ13
45. experiments: baselines
1. WSJ13
2. CR93E
3. AP111
.
.
MoveToFront (MTF) (Cormack et al 98)
extracts & judges docs from the selected run
stays in the run until a non-rel doc is found
100
WSJ13, CR93E
46. experiments: baselines
1. WSJ13
2. CR93E
3. AP111
.
.
MoveToFront (MTF) (Cormack et al 98)
extracts & judges docs from the selected run
stays in the run until a non-rel doc is found
100
WSJ13, CR93E, AP111
47. experiments: baselines
1. WSJ13
2. CR93E
3. AP111
.
.
MoveToFront (MTF) (Cormack et al 98)
extracts & judges docs from the selected run
stays in the run until a non-rel doc is found
when a non-rel doc is found, priority is decreased
100 99
WSJ13, CR93E, AP111
48. experiments: baselines
1. WSJ13
2. WSJ17
3. AP567
.
.
...
1. FT941
2. WSJ13
3. WSJ19
.
.
1. WSJ13
2. CR93E
3. AP111
.
.
MoveToFront (MTF) (Cormack et al 98)
and we jump again to another max priority run
1. ZF207
2. AP881
3. FT967
.
.
100 100 99 100
49. experiments: baselines
1. WSJ13
2. WSJ17
3. AP567
.
...
1. FT941
2. WSJ13
3. WSJ19
.
1. WSJ13
2. CR93E
3. AP111
.
Moffat et al.'s method (A) (Moffat et al 2007)
based on rank-biased precision (RBP)
sums a rank-dependent score for each doc
1. ZF207
2. AP881
3. FT967
.
score
0.20
0.16
0.13
.
50. experiments: baselines
1. WSJ13
2. WSJ17
3. AP567
.
...
1. FT941
2. WSJ13
3. WSJ19
.
1. WSJ13
2. CR93E
3. AP111
.
Moffat et al.'s method (A) (Moffat et al 2007)
based on rank-biased precision (RBP)
sums a rank-dependent score for each doc
1. ZF207
2. AP881
3. FT967
.
score
0.20
0.16
0.13
.
all docs are ranked by decreasing accummulated score
and the ranked list defines the order in which docs are judged
WSJ13: 0.20+0.16+0.20+...
51. experiments: baselines
Moffat et al.'s method (B) (Moffat et al 2007)
evolution over A's method
considers not only the rank-dependent doc's
contributions but also the runs' residuals
promotes the selection of docs from runs with many
unjudged docs
Moffat et al.'s method (C) (Moffat et al 2007)
evolution over B's method
considers not only the rank-dependent doc's and the residuals
promotes the selection of docs from effective runs
54. experiments: MTF vs bandit-based models
Random: weakest approach
BLA/UCB/ϵn
-greedy are suboptimal
(sophisticated exploration/exploitation trading
not needed)
MTF and MM: best performing methods
55. improved bandit-based models
MTF: forgets quickly about past rewards
(a single non-relevance doc triggers a jump)
non-stationary
bandit-based
solutions:
not all historical
rewards count the
same
MM-NS and BLA-NS
non-stationary
variants of MM and
BLA
56. stationary bandits
Beta( , ), , =1α β α β
rel docs add 1 to α
non-rel docs add 1 to β
(after n iterations)
Beta(αn
,βn
)
αn
=1+jrels
βn
=1+jrets
– jrels
jrels
: # judged relevant docs (retrieved by s)
jrets
: # judged docs (retrieved by s)
all judged docs count the same
non-stationary bandits
Beta( , ), , =1α β α β
jrels
= rate*jrels
+ reld
jrets
= rate*jrets
+ 1
(after n iterations)
Beta(αn
,βn
)
αn
=1+jrels
βn
=1+jrets
– jrels
rate>1: weights more early relevant docs
rate<1: weights more late relevant docs
rate=0: only the last judged doc counts
(BLA-NS, MM-NS)
rate=1: stationary version
58. conclusions
multi-arm bandits: formal & effective framework for
doc adjudication in a pooling-based evaluation
it's not good to increasingly reduce exploration
(UCB, ϵn
-greedy)
it's good to react quickly to non-relevant docs
(non-stationary variants)
60. reproduce our experiments & test new ideas!
http://tec.citius.usc.es/ir/code/pooling_bandits.html
(our R code, instructions, etc)
61. David E. Losada
Javier Parapar, Álvaro Barreiro
Feeling Lucky? Multi-armed bandits for Ordering Judgements
in Pooling-based Evaluation
Acknowledgements:
MsSaraKelly. picture pg 1 (modified).CC BY 2.0.
Sanofi Pasteur. picture pg 2 (modified).CC BY-NC-ND 2.0.
pedrik. picture pgs 3-5.CC BY 2.0.
Christa Lohman. picture pg 3 (left).CC BY-NC-ND 2.0.
Chris. picture pg 4 (tag cloud).CC BY 2.0.
Daniel Horacio Agostini. picture pg 5 (right).CC BY-NC-ND 2.0.
ScaarAT. picture pg 14.CC BY-NC-ND 2.0.
Sebastien Wiertz. picture pg 15 (modified).CC BY 2.0.
Willard. picture pg 16 (modified).CC BY-NC-ND 2.0.
Jose Luis Cernadas Iglesias. picture pg 17 (modified).CC BY 2.0.
Michelle Bender. picture pg 25 (left).CC BY-NC-ND 2.0.
Robert Levy. picture pg 25 (right).CC BY-NC-ND 2.0.
Simply Swim UK. picture pg 37.CC BY-SA 2.0.
Sarah J. Poe. picture pg 55.CC BY-ND 2.0.
Kate Brady. picture pg 58.CC BY 2.0.
August Brill. picture pg 59.CC BY 2.0.
This work was supported by the
“Ministerio de Economía y Competitividad”
of the Goverment of Spain and
FEDER Funds under
research projects
TIN2012-33867 and TIN2015-64282-R.