Notes 7-7-8
•
0 likes•335 views
This document discusses simulations and the Monte Carlo method. It defines a simulation as an experiment that models a situation while considering all possible outcomes. It defines the Monte Carlo method as utilizing relative frequencies from repeated trials to determine a simulation. The document instructs going to a wiki to complete an activity using a virtual manipulative called "Stick or Switch" to simulate strategies in a game.
Report
Share
Report
Share
Download to read offline
Recommended
Integrated Math 2 Section 9-3
This document provides examples and instructions for dividing monomials and polynomials by monomials. It begins with an essential question about dividing monomials and polynomials by monomials. It then provides the quotient rule for exponents and two examples of simplifying expressions by dividing monomials. The examples show dividing terms with the same bases and subtracting the exponents. It concludes by providing a homework assignment of problems dividing multiples of 3.
Integrated 2 Section 2-3
The document discusses writing variable expressions to represent word phrases and vice versa. It provides examples of commonly used variables like x and y and explains that variables are placeholders that represent unknown quantities. The document shows how to translate phrases like "four more than a number" to the expression x+4. It also gives the reverse translation from expressions like 3(x+10) to word phrases. Examples are provided to demonstrate translating real world word problems into variable expressions and vice versa. Homework problems from the textbook are assigned.
AA Section 8-2
This document discusses inverses of relations. It defines an inverse as the relationship obtained by reversing the order of coordinates in each ordered pair. An example shows how to find the inverse of a relation and determine if the original relation and inverse are functions. The inverse relation theorem states that the inverse can be found by switching x and y, the graph of the inverse is a reflection over y=x, and the domain of the inverse is the range of the original and vice versa. Another example shows finding the inverse of a function and graphing both. The horizontal line test is introduced to determine if an inverse is a function. Homework problems are assigned.
Section 1-4
The document discusses the benefits of exercise for mental health. Regular physical activity can help reduce anxiety and depression and improve mood and cognitive functioning. Exercise causes chemical changes in the brain that may help protect against mental illness and improve symptoms.
Notes 3-9
The document provides instructions and examples for calculating z-scores, which measure how many standard deviations a data point is from the mean. It gives the formula for calculating z-scores and walks through examples of finding the z-scores for individuals' test scores when given the mean and standard deviation. The examples are used to demonstrate that z-scores standardize data so the distribution has a mean of 0 and standard deviation of 1.
Notes 3-4
This document discusses symmetries of graphs and functions. It defines reflection symmetry, axis/line of symmetry, point/center of symmetry, and symmetry with respect to the x-axis or y-axis. It provides examples of proving symmetry of graphs like y=x^2 and y=x. It also defines even and odd functions and discusses using the graph-translation theorem to find asymptotes. The homework assigned is problems 1 through 21 on page 183.
Notes 6-3
The document contains examples of solving logarithmic equations without a calculator by rewriting them as exponential equations and evaluating. It begins with warmup problems solving for the exponent in equations like 10 = 0.0001a. It then introduces the definition of logarithms and explores properties like domain, range, and that logarithms are strictly increasing. It contains examples of evaluating logarithms and solving logarithmic equations by rewriting them as exponential equations and evaluating.
Chapter11a
Simulation may be defined as a technique that imitates the
operation of a real-world system as it evolves over time. This is normally
done by developing a simulation model.
A simulation model take the form of a set of assumptions about
the operation of the system, expressed as mathematical or logical
relations between the objects of interest in the system.
Recommended
Integrated Math 2 Section 9-3
This document provides examples and instructions for dividing monomials and polynomials by monomials. It begins with an essential question about dividing monomials and polynomials by monomials. It then provides the quotient rule for exponents and two examples of simplifying expressions by dividing monomials. The examples show dividing terms with the same bases and subtracting the exponents. It concludes by providing a homework assignment of problems dividing multiples of 3.
Integrated 2 Section 2-3
The document discusses writing variable expressions to represent word phrases and vice versa. It provides examples of commonly used variables like x and y and explains that variables are placeholders that represent unknown quantities. The document shows how to translate phrases like "four more than a number" to the expression x+4. It also gives the reverse translation from expressions like 3(x+10) to word phrases. Examples are provided to demonstrate translating real world word problems into variable expressions and vice versa. Homework problems from the textbook are assigned.
AA Section 8-2
This document discusses inverses of relations. It defines an inverse as the relationship obtained by reversing the order of coordinates in each ordered pair. An example shows how to find the inverse of a relation and determine if the original relation and inverse are functions. The inverse relation theorem states that the inverse can be found by switching x and y, the graph of the inverse is a reflection over y=x, and the domain of the inverse is the range of the original and vice versa. Another example shows finding the inverse of a function and graphing both. The horizontal line test is introduced to determine if an inverse is a function. Homework problems are assigned.
Section 1-4
The document discusses the benefits of exercise for mental health. Regular physical activity can help reduce anxiety and depression and improve mood and cognitive functioning. Exercise causes chemical changes in the brain that may help protect against mental illness and improve symptoms.
Notes 3-9
The document provides instructions and examples for calculating z-scores, which measure how many standard deviations a data point is from the mean. It gives the formula for calculating z-scores and walks through examples of finding the z-scores for individuals' test scores when given the mean and standard deviation. The examples are used to demonstrate that z-scores standardize data so the distribution has a mean of 0 and standard deviation of 1.
Notes 3-4
This document discusses symmetries of graphs and functions. It defines reflection symmetry, axis/line of symmetry, point/center of symmetry, and symmetry with respect to the x-axis or y-axis. It provides examples of proving symmetry of graphs like y=x^2 and y=x. It also defines even and odd functions and discusses using the graph-translation theorem to find asymptotes. The homework assigned is problems 1 through 21 on page 183.
Notes 6-3
The document contains examples of solving logarithmic equations without a calculator by rewriting them as exponential equations and evaluating. It begins with warmup problems solving for the exponent in equations like 10 = 0.0001a. It then introduces the definition of logarithms and explores properties like domain, range, and that logarithms are strictly increasing. It contains examples of evaluating logarithms and solving logarithmic equations by rewriting them as exponential equations and evaluating.
Chapter11a
Simulation may be defined as a technique that imitates the
operation of a real-world system as it evolves over time. This is normally
done by developing a simulation model.
A simulation model take the form of a set of assumptions about
the operation of the system, expressed as mathematical or logical
relations between the objects of interest in the system.
Simulation theory
The document discusses simulation theory and the Monte Carlo method of simulation. It defines simulation as imitating reality and explains that simulation is used to understand complex systems when real experimentation is not possible or analytical solutions are unknown. It describes the Monte Carlo method as using probability distributions and random numbers to simulate random systems. The key steps are: (1) obtaining variable probabilities from data, (2) converting to cumulative probabilities, (3) generating random numbers, (4) mapping random numbers to probability intervals to determine outcomes, and (5) repeating simulations. An example demonstrates using cumulative probabilities and random numbers to simulate daily cake demand for a bakery.
MONTE CARLO SimulationChapter 14 PowerPointManagement
MONTE CARLO Simulation
Chapter 14 PowerPoint
Management
Science
The Art of Modeling with Spreadsheets
Stephen G. POWELL
Kenneth R. BAKER
Compatible with Analytic Solver Platform
Fourth Edition
Introduction
Monte Carlo simulation is an important and flexible technique for modeling situations in which uncertainty is a key factor.
Analytic Solver Platform provides the capability to implement Monte Carlo simulation in spreadsheet models.
Simulation can describe not only what the outcomes of a given decision could be, but also the probabilities with which these outcomes will occur.
In fact, the result of a simulation is the entire probability distribution of outcomes.
In a sense, simulation is an advanced form of sensitivity analysis in which we attach a probability to each possible outcome.
Chapter 14
Copyright © 2013 John Wiley & Sons, Inc.
2
Introduction
We often wish to determine the probability of a particular set of outcomes.
Such “tail probabilities” are often suitable measures of the risk associated with a decision.
While decision trees provide a simple means for analyzing decisions with uncertainty and risk, simulation is the tool of choice when there are a large number of uncertainties, especially when these are represented by continuous distributions.
Simulation is also a practical method when the underlying model is complex.
However, it is important to realize that, just as with decision trees, the result of a simulation is a probability distribution for each outcome.
Analyzing these distributions and extracting managerial insights is an important part of the art of simulation.
Chapter 14
Copyright © 2013 John Wiley & Sons, Inc.
3
Essential Steps in a Simulation
Chapter 14
Copyright © 2013 John Wiley & Sons, Inc.
4
Start with a base case model and determine which of the input parameters to represent as uncertain.
Develop probability distributions for those inputs.
Take random samples from those inputs and calculate the resulting output, repeating the process until a clear picture of the output distribution emerges.
Create a histogram of the outcomes and interpret it.
Simulation provides two essential pieces of information: mean values (also called expected values) and tail probabilities (e.g., the probability of a positive profit).
Modeling Tip: Creating Simulation Models
Beginners to simulation modeling often find it difficult to build an initial spreadsheet model. This may be because a simulation model must correctly evaluate a large or even infinite number of different random inputs.
One useful trick is to fix the random inputs at some arbitrary value and build a spreadsheet model to evaluate those inputs.
This step allows us to build and debug a spreadsheet with no uncertainty, which is a simpler task than debugging a simulation model.
Only after we have debugged this model do we introduce uncertainties (e.g., fluctuations in sales).
Chapter 14
Copyright © 2013 John Wiley & Sons, Inc.
5
Sensitivity Analys ...
Risk_Management_Final_Report
This document summarizes a risk analysis of options for a wastewater treatment plant for a gold mine. A Monte Carlo simulation model analyzed the costs of three options: a used skid plant, a new skid plant, and a new fixed plant. The analysis found that a new skid plant had the lowest expected cost and was least impacted by parameter variations, making it the recommended option. A sensitivity analysis identified key cost drivers to incorporate into the Monte Carlo modeling. Risk measures including value-at-risk and mean upper semi-deviation supported choosing the new skid plant as the lowest risk option.
Property Industry Project Thesis
This document presents a research thesis that investigates using Monte Carlo simulation as an asset allocation tool for real estate portfolios during different economic periods. The thesis acknowledges those who provided guidance and support. It includes an introduction outlining the research problem and question, as well as the methodology, literature review, research design, data analysis, conclusion and references. The research aims to determine if altering real estate asset allocation based on Monte Carlo simulation during growth, decline and stable periods can enhance portfolio performance compared to a static allocation. Portfolio performance is measured using the Sharpe ratio to assess risk-adjusted returns.
Simulation.pps.pdf
Simulation is a quantitative method used to model real-world processes when mathematical solutions are not possible. It involves developing a model of the process and running experiments to predict how the process behaves over time. There are different types of simulation models, including deterministic and stochastic models as well as static and dynamic models. The steps in simulation include defining the problem, formulating a model, testing the model, collecting data, running simulations, analyzing results, and validating the simulation. Monte Carlo simulation uses random numbers to solve probabilistic problems and involves selecting measures of effectiveness, variables, distributions, random numbers, and repeating to generate values. Simulation can be used in inventory control to experiment with inventory policies and minimize costs under uncertainty.
model simulating
Here are the steps to complete the homework:
1. Define the inputs:
- Number of sales reps (num_reps)
- Target sales amount per rep (distributed normally)
- Commission rate if meet target
- Commission rate if exceed target
- Standard deviation of sales amounts
2. Use NumPy to generate random sales amounts based on the inputs
- Generate random numbers based on normal distribution
- Multiply random numbers by target amounts
3. Calculate commissions
- Compare sales to targets
- Apply different commission rates below and above target
- Sum commissions
4. Run simulation multiple times (e.g. 10,000) to get distribution
- Record commissions from each run
- Calculate average
Whitebox
The document discusses various white box testing techniques:
1. Statement testing executes all statements in the code.
2. Branch/Decision Testing executes all outcomes (true/false) of decision points.
3. Branch Condition Combination Testing checks that each combination of inputs for a condition is tested to cover all branches.
4. Modified Condition Combination Testing independently tests each atomic condition to check its effect on the decision outcome.
Stochastic Modeling - Model Risk - Sampling Error - Scenario Reduction
This document discusses model risk, sampling error, and scenario reduction techniques for stochastic modeling. It begins by defining model risk and its components. It then discusses general principles for managing model risk, including assigning model owners, vetting models, segregating duties, maintaining model inventories and documentation, identifying and controlling risks, reviewing models, and backtesting. It also addresses how sampling error can impact stochastic models and the benefits and limitations of using representative scenarios to reduce computational requirements.
Introduction to free and open source Chemical Process Simulators - (DWSIM & C...
Learn the fundamentals of any chemical process simulator software by means of free and open source software as an alternative to Aspen, Aspen HYSYS, etc. We will be using DWSIM (open source and free) and COCO Simulator (freeware) for this course. Material is licensed under CC BY-NC-SA 3.0.
You can find more learning material for chemical engineers in http://CAChemE.org
TAGs: chemical , process , simulator , engineering , coco , dwsim , hysys , aspen , prosim , theory, software, free, open, source, flowsheet, course
08validation
This document discusses different approaches to validating models, including empirical analysis of data, building models to simulate real-world systems, and comparing model outputs to real-world observations. It describes validation by construction, validation by results such as prediction experiments, and validation through subjective evaluation of how well a model predicts. The overall goal of validation is to test how accurately a model can predict and determine if its predictions are good enough to be useful.
Coverage and Introduction to UVM
This document provides an introduction to verification and the Universal Verification Methodology (UVM). It discusses different types of verification including simulation, functional coverage, and code coverage. It describes how simulators work and their limitations in approximating physical hardware. It also covers topics like event-driven simulation, cycle-based simulation, co-simulation, and different types of coverage metrics used to ensure a design is fully tested.
Robustness Metrics for ML Models based on Deep Learning Methods
Robustness Metrics for ML
Models based on Deep
Learning Methods
Davide Posillipo
Alkemy - Data Science Milan Meetup - AI Guild
PowerWorld 15. Transient Stability Quickstart
This document provides a quick introduction to using PowerWorld Simulator for transient stability analysis. It begins with an overview and goals, then demonstrates how to convert a power flow case to a transient stability case by adding a classical machine model to a generator. It shows how to specify a fault event, choose results to view, run the simulation, and view time-series and minimum/maximum results. Finally, it provides an example of changing the model and fault to replicate an example from a textbook, and compares the resulting rotor angle plot.
Model Risk Management : Best Practices
Model risk and the importance of model risk management has gotten significant attention in the last few years. As financial companies increase their reliance on quants and quantitative models for decision making, they are increasingly exposed to model risk and are looking for ways to mitigate it. The financial crisis of 2008 and various high profile financial accidents due to model failures has brought model risk management to the forefront as an important topic to be addressed. Many regulatory efforts (Solvency II, Basel III, Dodd-Frank etc.) have been initiated obligating banks and financial institutions to incorporate formal model risk management programs to address model risk. In this talk, we will discuss the key aspects of model verification and validation and discuss best practices for stress and scenario testing for model verification and validation for MATLAB-based models. These best practices are meant to provide practical tips for companies embarking on a formal model risk management program or enhancing their model risk methodologies to address the new realities.
Verification of simulation computer program by ashish gangwar (8445059669)
This Is My First Slid on Slide Share .
ASHISH GANGWAR
BCA MCA
8445059669
Thank you slide share.
Recovery Scenario in QTP
Recovery scenarios in QTP allow users to define actions for the test to take in the event of errors or unexpected events during a test run. They specify trigger events, recovery operations, and options for continuing the test run after recovery. Common trigger events include pop-up windows, object state changes, test errors, and application crashes. Recovery operations can include clicking buttons, closing applications, or calling functions. Recovery scenarios help automate recovering from issues to complete testing without manual intervention.
Evaluating Model Testing and Model Checking for Finding Requirements Violatio...
Model checking and model testing were compared for finding requirements violations in Simulink models. Model checking uses formal methods to exhaustively verify properties, but may not be applicable to some model constructs. Model testing uses meta-heuristic search to dynamically execute models and identify failures. Both approaches were applied to an industrial Simulink benchmark containing 11 models and 92 requirements. Model testing found violations of 40 requirements, while model checking could exhaustively prove 41 requirements and found violations of 23 requirements, but could not handle one of the models.
Uppaal Lite
The document provides an overview of Uppaal, a tool for modeling, validating and verifying real-time systems. Uppaal allows modeling systems as a collection of timed automata communicating through channels. It provides an integrated environment for modeling, simulation and verification of requirements. The verifier checks safety and liveness properties through reachability analysis. An example of modeling a simple phone with calls and SMS is presented and its requirements are verified using Uppaal.
Ludwig: A code-free deep learning toolbox | Piero Molino, Uber AI
The talk will introduce Ludwig, a deep learning toolbox that allows to train models and to use them for prediction without the need to write code. It is unique in its ability to help make deep learning easier to understand for non-experts and enable faster model improvement iteration cycles for experienced machine learning developers and researchers alike. By using Ludwig, experts and researchers can simplify the prototyping process and streamline data processing so that they can focus on developing deep learning architectures.
Bio:
Piero Molino is a Senior Research Scientist at Uber AI with focus on machine learning for language and dialogue. Piero completed a PhD on Question Answering at the University of Bari, Italy. Founded QuestionCube, a startup that built a framework for semantic search and QA. Worked for Yahoo Labs in Barcelona on learning to rank, IBM Watson in New York on natural language processing with deep learning and then joined Geometric Intelligence, where he worked on grounded language understanding. After Uber acquired Geometric Intelligence, he became one of the founding members of Uber AI Labs.
Md simulation and stochastic simulation
Stochastic simulation involves modeling systems with random variables. It generates random values for insertion into models to understand probable outcomes. Molecular dynamic simulation computationally simulates atom and molecule movements over time based on forces. It provides time-dependent behavior analysis of biological molecules to study structure, dynamics, and thermodynamics without harming environments. Both methods help understand complex systems through numerous replications under varying scenarios.
Geometry Section 1-5
The document defines and provides examples of angle relationships including adjacent angles, linear pairs, vertical angles, complementary angles, and supplementary angles. It defines each term and provides examples of identifying angle pairs that satisfy each relationship. It also includes examples of using properties of these angle relationships to solve problems, such as finding missing angle measures.
Geometry Section 1-4
The document defines various terms related to angle measure including ray, angle, vertex, acute angle, obtuse angle, and angle bisector. It then provides examples measuring angles in a figure and solving an equation involving angle measures.
More Related Content
Similar to Notes 7-7-8
Simulation theory
The document discusses simulation theory and the Monte Carlo method of simulation. It defines simulation as imitating reality and explains that simulation is used to understand complex systems when real experimentation is not possible or analytical solutions are unknown. It describes the Monte Carlo method as using probability distributions and random numbers to simulate random systems. The key steps are: (1) obtaining variable probabilities from data, (2) converting to cumulative probabilities, (3) generating random numbers, (4) mapping random numbers to probability intervals to determine outcomes, and (5) repeating simulations. An example demonstrates using cumulative probabilities and random numbers to simulate daily cake demand for a bakery.
MONTE CARLO SimulationChapter 14 PowerPointManagement
MONTE CARLO Simulation
Chapter 14 PowerPoint
Management
Science
The Art of Modeling with Spreadsheets
Stephen G. POWELL
Kenneth R. BAKER
Compatible with Analytic Solver Platform
Fourth Edition
Introduction
Monte Carlo simulation is an important and flexible technique for modeling situations in which uncertainty is a key factor.
Analytic Solver Platform provides the capability to implement Monte Carlo simulation in spreadsheet models.
Simulation can describe not only what the outcomes of a given decision could be, but also the probabilities with which these outcomes will occur.
In fact, the result of a simulation is the entire probability distribution of outcomes.
In a sense, simulation is an advanced form of sensitivity analysis in which we attach a probability to each possible outcome.
Chapter 14
Copyright © 2013 John Wiley & Sons, Inc.
2
Introduction
We often wish to determine the probability of a particular set of outcomes.
Such “tail probabilities” are often suitable measures of the risk associated with a decision.
While decision trees provide a simple means for analyzing decisions with uncertainty and risk, simulation is the tool of choice when there are a large number of uncertainties, especially when these are represented by continuous distributions.
Simulation is also a practical method when the underlying model is complex.
However, it is important to realize that, just as with decision trees, the result of a simulation is a probability distribution for each outcome.
Analyzing these distributions and extracting managerial insights is an important part of the art of simulation.
Chapter 14
Copyright © 2013 John Wiley & Sons, Inc.
3
Essential Steps in a Simulation
Chapter 14
Copyright © 2013 John Wiley & Sons, Inc.
4
Start with a base case model and determine which of the input parameters to represent as uncertain.
Develop probability distributions for those inputs.
Take random samples from those inputs and calculate the resulting output, repeating the process until a clear picture of the output distribution emerges.
Create a histogram of the outcomes and interpret it.
Simulation provides two essential pieces of information: mean values (also called expected values) and tail probabilities (e.g., the probability of a positive profit).
Modeling Tip: Creating Simulation Models
Beginners to simulation modeling often find it difficult to build an initial spreadsheet model. This may be because a simulation model must correctly evaluate a large or even infinite number of different random inputs.
One useful trick is to fix the random inputs at some arbitrary value and build a spreadsheet model to evaluate those inputs.
This step allows us to build and debug a spreadsheet with no uncertainty, which is a simpler task than debugging a simulation model.
Only after we have debugged this model do we introduce uncertainties (e.g., fluctuations in sales).
Chapter 14
Copyright © 2013 John Wiley & Sons, Inc.
5
Sensitivity Analys ...
Risk_Management_Final_Report
This document summarizes a risk analysis of options for a wastewater treatment plant for a gold mine. A Monte Carlo simulation model analyzed the costs of three options: a used skid plant, a new skid plant, and a new fixed plant. The analysis found that a new skid plant had the lowest expected cost and was least impacted by parameter variations, making it the recommended option. A sensitivity analysis identified key cost drivers to incorporate into the Monte Carlo modeling. Risk measures including value-at-risk and mean upper semi-deviation supported choosing the new skid plant as the lowest risk option.
Property Industry Project Thesis
This document presents a research thesis that investigates using Monte Carlo simulation as an asset allocation tool for real estate portfolios during different economic periods. The thesis acknowledges those who provided guidance and support. It includes an introduction outlining the research problem and question, as well as the methodology, literature review, research design, data analysis, conclusion and references. The research aims to determine if altering real estate asset allocation based on Monte Carlo simulation during growth, decline and stable periods can enhance portfolio performance compared to a static allocation. Portfolio performance is measured using the Sharpe ratio to assess risk-adjusted returns.
Simulation.pps.pdf
Simulation is a quantitative method used to model real-world processes when mathematical solutions are not possible. It involves developing a model of the process and running experiments to predict how the process behaves over time. There are different types of simulation models, including deterministic and stochastic models as well as static and dynamic models. The steps in simulation include defining the problem, formulating a model, testing the model, collecting data, running simulations, analyzing results, and validating the simulation. Monte Carlo simulation uses random numbers to solve probabilistic problems and involves selecting measures of effectiveness, variables, distributions, random numbers, and repeating to generate values. Simulation can be used in inventory control to experiment with inventory policies and minimize costs under uncertainty.
model simulating
Here are the steps to complete the homework:
1. Define the inputs:
- Number of sales reps (num_reps)
- Target sales amount per rep (distributed normally)
- Commission rate if meet target
- Commission rate if exceed target
- Standard deviation of sales amounts
2. Use NumPy to generate random sales amounts based on the inputs
- Generate random numbers based on normal distribution
- Multiply random numbers by target amounts
3. Calculate commissions
- Compare sales to targets
- Apply different commission rates below and above target
- Sum commissions
4. Run simulation multiple times (e.g. 10,000) to get distribution
- Record commissions from each run
- Calculate average
Whitebox
The document discusses various white box testing techniques:
1. Statement testing executes all statements in the code.
2. Branch/Decision Testing executes all outcomes (true/false) of decision points.
3. Branch Condition Combination Testing checks that each combination of inputs for a condition is tested to cover all branches.
4. Modified Condition Combination Testing independently tests each atomic condition to check its effect on the decision outcome.
Stochastic Modeling - Model Risk - Sampling Error - Scenario Reduction
This document discusses model risk, sampling error, and scenario reduction techniques for stochastic modeling. It begins by defining model risk and its components. It then discusses general principles for managing model risk, including assigning model owners, vetting models, segregating duties, maintaining model inventories and documentation, identifying and controlling risks, reviewing models, and backtesting. It also addresses how sampling error can impact stochastic models and the benefits and limitations of using representative scenarios to reduce computational requirements.
Introduction to free and open source Chemical Process Simulators - (DWSIM & C...
Learn the fundamentals of any chemical process simulator software by means of free and open source software as an alternative to Aspen, Aspen HYSYS, etc. We will be using DWSIM (open source and free) and COCO Simulator (freeware) for this course. Material is licensed under CC BY-NC-SA 3.0.
You can find more learning material for chemical engineers in http://CAChemE.org
TAGs: chemical , process , simulator , engineering , coco , dwsim , hysys , aspen , prosim , theory, software, free, open, source, flowsheet, course
08validation
This document discusses different approaches to validating models, including empirical analysis of data, building models to simulate real-world systems, and comparing model outputs to real-world observations. It describes validation by construction, validation by results such as prediction experiments, and validation through subjective evaluation of how well a model predicts. The overall goal of validation is to test how accurately a model can predict and determine if its predictions are good enough to be useful.
Coverage and Introduction to UVM
This document provides an introduction to verification and the Universal Verification Methodology (UVM). It discusses different types of verification including simulation, functional coverage, and code coverage. It describes how simulators work and their limitations in approximating physical hardware. It also covers topics like event-driven simulation, cycle-based simulation, co-simulation, and different types of coverage metrics used to ensure a design is fully tested.
Robustness Metrics for ML Models based on Deep Learning Methods
Robustness Metrics for ML
Models based on Deep
Learning Methods
Davide Posillipo
Alkemy - Data Science Milan Meetup - AI Guild
PowerWorld 15. Transient Stability Quickstart
This document provides a quick introduction to using PowerWorld Simulator for transient stability analysis. It begins with an overview and goals, then demonstrates how to convert a power flow case to a transient stability case by adding a classical machine model to a generator. It shows how to specify a fault event, choose results to view, run the simulation, and view time-series and minimum/maximum results. Finally, it provides an example of changing the model and fault to replicate an example from a textbook, and compares the resulting rotor angle plot.
Model Risk Management : Best Practices
Model risk and the importance of model risk management has gotten significant attention in the last few years. As financial companies increase their reliance on quants and quantitative models for decision making, they are increasingly exposed to model risk and are looking for ways to mitigate it. The financial crisis of 2008 and various high profile financial accidents due to model failures has brought model risk management to the forefront as an important topic to be addressed. Many regulatory efforts (Solvency II, Basel III, Dodd-Frank etc.) have been initiated obligating banks and financial institutions to incorporate formal model risk management programs to address model risk. In this talk, we will discuss the key aspects of model verification and validation and discuss best practices for stress and scenario testing for model verification and validation for MATLAB-based models. These best practices are meant to provide practical tips for companies embarking on a formal model risk management program or enhancing their model risk methodologies to address the new realities.
Verification of simulation computer program by ashish gangwar (8445059669)
This Is My First Slid on Slide Share .
ASHISH GANGWAR
BCA MCA
8445059669
Thank you slide share.
Recovery Scenario in QTP
Recovery scenarios in QTP allow users to define actions for the test to take in the event of errors or unexpected events during a test run. They specify trigger events, recovery operations, and options for continuing the test run after recovery. Common trigger events include pop-up windows, object state changes, test errors, and application crashes. Recovery operations can include clicking buttons, closing applications, or calling functions. Recovery scenarios help automate recovering from issues to complete testing without manual intervention.
Evaluating Model Testing and Model Checking for Finding Requirements Violatio...
Model checking and model testing were compared for finding requirements violations in Simulink models. Model checking uses formal methods to exhaustively verify properties, but may not be applicable to some model constructs. Model testing uses meta-heuristic search to dynamically execute models and identify failures. Both approaches were applied to an industrial Simulink benchmark containing 11 models and 92 requirements. Model testing found violations of 40 requirements, while model checking could exhaustively prove 41 requirements and found violations of 23 requirements, but could not handle one of the models.
Uppaal Lite
The document provides an overview of Uppaal, a tool for modeling, validating and verifying real-time systems. Uppaal allows modeling systems as a collection of timed automata communicating through channels. It provides an integrated environment for modeling, simulation and verification of requirements. The verifier checks safety and liveness properties through reachability analysis. An example of modeling a simple phone with calls and SMS is presented and its requirements are verified using Uppaal.
Ludwig: A code-free deep learning toolbox | Piero Molino, Uber AI
The talk will introduce Ludwig, a deep learning toolbox that allows to train models and to use them for prediction without the need to write code. It is unique in its ability to help make deep learning easier to understand for non-experts and enable faster model improvement iteration cycles for experienced machine learning developers and researchers alike. By using Ludwig, experts and researchers can simplify the prototyping process and streamline data processing so that they can focus on developing deep learning architectures.
Bio:
Piero Molino is a Senior Research Scientist at Uber AI with focus on machine learning for language and dialogue. Piero completed a PhD on Question Answering at the University of Bari, Italy. Founded QuestionCube, a startup that built a framework for semantic search and QA. Worked for Yahoo Labs in Barcelona on learning to rank, IBM Watson in New York on natural language processing with deep learning and then joined Geometric Intelligence, where he worked on grounded language understanding. After Uber acquired Geometric Intelligence, he became one of the founding members of Uber AI Labs.
Md simulation and stochastic simulation
Stochastic simulation involves modeling systems with random variables. It generates random values for insertion into models to understand probable outcomes. Molecular dynamic simulation computationally simulates atom and molecule movements over time based on forces. It provides time-dependent behavior analysis of biological molecules to study structure, dynamics, and thermodynamics without harming environments. Both methods help understand complex systems through numerous replications under varying scenarios.
Similar to Notes 7-7-8 (20)
MONTE CARLO SimulationChapter 14 PowerPointManagement
MONTE CARLO SimulationChapter 14 PowerPointManagement
Stochastic Modeling - Model Risk - Sampling Error - Scenario Reduction
Stochastic Modeling - Model Risk - Sampling Error - Scenario Reduction
Introduction to free and open source Chemical Process Simulators - (DWSIM & C...
Introduction to free and open source Chemical Process Simulators - (DWSIM & C...
Robustness Metrics for ML Models based on Deep Learning Methods
Robustness Metrics for ML Models based on Deep Learning Methods
Verification of simulation computer program by ashish gangwar (8445059669)
Verification of simulation computer program by ashish gangwar (8445059669)
Evaluating Model Testing and Model Checking for Finding Requirements Violatio...
Evaluating Model Testing and Model Checking for Finding Requirements Violatio...
Ludwig: A code-free deep learning toolbox | Piero Molino, Uber AI
Ludwig: A code-free deep learning toolbox | Piero Molino, Uber AI
More from Jimbo Lamb
Geometry Section 1-5
The document defines and provides examples of angle relationships including adjacent angles, linear pairs, vertical angles, complementary angles, and supplementary angles. It defines each term and provides examples of identifying angle pairs that satisfy each relationship. It also includes examples of using properties of these angle relationships to solve problems, such as finding missing angle measures.
Geometry Section 1-4
The document defines various terms related to angle measure including ray, angle, vertex, acute angle, obtuse angle, and angle bisector. It then provides examples measuring angles in a figure and solving an equation involving angle measures.
Geometry Section 1-3
This document discusses finding points and midpoints on line segments. It defines midpoint as the point halfway between two endpoints and provides the formula to calculate it. Several examples are given to demonstrate how to find the midpoint of a segment, locate a point at a fractional distance from one endpoint, and find a point where the ratio of distances from the endpoints is a given ratio. The key concepts covered are calculating midpoints using averages of x- and y-coordinates, and setting up and solving equations to locate interior points using fractional or ratio distances along a segment.
Geometry Section 1-2
This document defines key vocabulary terms related to line segments and distance. It defines a line segment as a portion of a line distinguished by endpoints, and defines betweenness of points and the term "between." It also defines congruent segments, constructions, distance, and irrational numbers. Several examples are provided to demonstrate calculating distances between points on a number line, using a ruler to measure segments, and applying the Pythagorean theorem.
Geometry Section 1-2
This document defines key vocabulary terms related to line segments and distance, including line segment, betweenness of points, congruent segments, and distance formula. It provides examples of calculating distances between points on number lines and using the Pythagorean theorem to find distances between points graphed on a coordinate plane. Examples include measuring line segments with a ruler, finding distances by adding or subtracting measures, and applying the distance formula and Pythagorean theorem to solve for unknown distances.
Geometry Section 1-1
This document introduces basic geometry concepts such as points, lines, planes, and their intersections. It defines a point as having no size or shape, a line as an infinite set of collinear points, and a plane as a flat surface extending indefinitely. Examples demonstrate identifying geometric shapes from real-world objects and graphing points and lines on a coordinate plane. The summary defines key terms and provides examples of geometric concepts and relationships.
Algebra 2 Section 5-3
The document discusses inverse functions and relations. It defines an inverse relation as one where the coordinates of a relation are switched, and an inverse function as one where the domain and range of a function are switched. It provides examples of finding the inverse of specific relations and functions by switching their coordinates or domain and range. It also discusses how to determine if two functions are inverses using their graphs and the horizontal line test.
Algebra 2 Section 5-2
The document discusses composition of functions. It defines composition of functions as using the output of one function as the input of another. It provides an example of composing two functions f and g, showing the steps of evaluating f(g(x)) and g(f(x)) at different values of x. Another example is given with two functions defined by sets of ordered pairs, finding the compositions f∘g and g∘f by evaluating them at different inputs and stating their domains and ranges.
Algebra 2 Section 5-1
The document discusses operations that can be performed on functions, including addition, subtraction, multiplication, and division. Definitions of each operation are provided, along with examples of applying the operations to specific functions. Addition of functions involves adding the outputs of each function, subtraction involves subtracting the outputs, multiplication involves multiplying the outputs, and division involves dividing the outputs given the denominator function is not equal to 0. Several examples are worked through applying the different operations to functions like f(x)=2x and g(x)=-x+5. The examples also demonstrate evaluating composite functions and restricting domains as needed.
Algebra 2 Section 4-9
The document discusses determining the number and type of roots of polynomial equations. It states that every polynomial with degree greater than zero has at least one root in the set of complex numbers according to the Fundamental Theorem of Algebra. Descartes' Rule of Signs is introduced, which relates the number of changes in sign of a polynomial's terms to its possible positive and negative real roots. An example problem is worked through applying these concepts to determine the possible number of positive, negative, and imaginary roots.
Algebra 2 Section 4-8
This document discusses synthetic division and the remainder and factor theorems. It provides examples of using synthetic division to evaluate functions, determine the number of terms in a sequence, and factor polynomials. The key steps of synthetic division are shown, along with checking the remainder and determining common factors. Three examples are worked through to demonstrate these concepts.
Algebra 2 Section 4-6
This document discusses solving polynomial equations by factoring polynomials. It begins with essential questions and vocabulary about factoring polynomials and solving polynomial equations by factoring. It then provides the number of terms in a polynomial and the corresponding factoring technique that can be used. Examples of factoring various polynomials are also provided. The document aims to teach students how to factor polynomials and solve polynomial equations by factoring.
Geometry Section 6-6
The document defines key terms and theorems related to trapezoids and kites. It provides definitions for trapezoid, bases, legs of a trapezoid, base angles, isosceles trapezoid, midsegment of a trapezoid, and kite. It also lists theorems about properties of isosceles trapezoids and kites. Two examples problems are included, one finding measures of an isosceles trapezoid and another showing a quadrilateral is a trapezoid.
Geometry Section 6-5
The document discusses rhombi and squares. It defines a rhombus as a parallelogram with four congruent sides and gives its properties. A square is defined as a parallelogram with four right angles and four congruent sides. The document provides theorems for identifying rhombi and squares. It then gives examples of using the properties and theorems to determine if a shape is a rhombus, rectangle, or square.
Geometry Section 6-4
The document discusses properties of rectangles. A rectangle is defined as a parallelogram with four right angles. The key properties are that opposite sides are parallel and congruent, opposite angles are congruent, and consecutive angles are supplementary. The diagonals of a rectangle bisect each other and are congruent. Theorems are presented regarding the diagonals of rectangles. Examples apply the properties of rectangles to find missing side lengths, angles, and diagonals. One example uses the distance formula and slope to determine if a quadrilateral is a rectangle.
Geometry Section 6-3
The document discusses properties of parallelograms and provides examples of determining if a quadrilateral is a parallelogram. It defines four theorems for identifying parallelograms based on opposite sides, opposite angles, bisecting diagonals, and parallel/congruent sides. Examples solve systems of equations to find values of variables such that the quadrilaterals satisfy parallelogram properties. One example uses slopes of side segments to show a quadrilateral is a parallelogram due to parallel opposite sides.
Geometry Section 6-2
The document discusses properties of parallelograms. It defines a parallelogram as a quadrilateral with two pairs of parallel sides. It then lists several properties of parallelograms: opposite sides are congruent, opposite angles are congruent, consecutive angles are supplementary, and if one angle is a right angle all angles are right angles. It also discusses properties of diagonals in parallelograms, including that diagonals bisect each other and divide the parallelogram into two congruent triangles. Several examples demonstrate using these properties to solve problems about parallelograms.
Geometry Section 6-1
The document summarizes key concepts about polygons, including:
- The sum of the interior angles of a polygon with n sides is (n-2)180 degrees.
- The sum of the exterior angles of a polygon is 360 degrees.
- Examples are provided to demonstrate calculating sums of interior/exterior angles and finding missing angle measures using angle sums.
- Regular polygons are defined by their number of sides.
Algebra 2 Section 4-5
The document discusses analyzing graphs of polynomial functions. It provides examples of locating real zeros of polynomials using the location principle and estimating relative maxima and minima. Example 1 analyzes the polynomial f(x) = x^4 - x^3 - 4x^2 + 1 and locates its real zeros between consecutive integer values. Example 2 graphs the polynomial f(x) = x^3 - 3x^2 + 5 and estimates the x-coordinates of relative maxima and minima.
Algebra 2 Section 4-4
This document discusses polynomial functions. It defines key terms like polynomial in one variable, leading coefficient, and polynomial function. It provides examples of power functions of varying degrees like quadratic, cubic, quartic and quintic functions. The document also includes examples of evaluating polynomial functions, finding degrees and leading coefficients, graphing polynomial functions from tables of values, and describing properties of graphs.
More from Jimbo Lamb (20)
Recently uploaded
GraphRAG for Life Science to increase LLM accuracy
GraphRAG for life science domain, where you retriever information from biomedical knowledge graphs using LLMs to increase the accuracy and performance of generated answers
How to Get CNIC Information System with Paksim Ga.pptx
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.
Programming Foundation Models with DSPy - Meetup Slides
Prompting language models is hard, while programming language models is easy. In this talk, I will discuss the state-of-the-art framework DSPy for programming foundation models with its powerful optimizers and runtime constraint system.
みなさんこんにちはこれ何文字まで入るの？40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの？えこ...
ここ3000字までしか入らないけどタイトルの方がたくさん文字入ると思います。
HCL Notes and Domino License Cost Reduction in the World of DLAU
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-and-domino-license-cost-reduction-in-the-world-of-dlau/
The introduction of DLAU and the CCB & CCX licensing model caused quite a stir in the HCL community. As a Notes and Domino customer, you may have faced challenges with unexpected user counts and license costs. You probably have questions on how this new licensing approach works and how to benefit from it. Most importantly, you likely have budget constraints and want to save money where possible. Don’t worry, we can help with all of this!
We’ll show you how to fix common misconfigurations that cause higher-than-expected user counts, and how to identify accounts which you can deactivate to save money. There are also frequent patterns that can cause unnecessary cost, like using a person document instead of a mail-in for shared mailboxes. We’ll provide examples and solutions for those as well. And naturally we’ll explain the new licensing model.
Join HCL Ambassador Marc Thomas in this webinar with a special guest appearance from Franz Walder. It will give you the tools and know-how to stay on top of what is going on with Domino licensing. You will be able lower your cost through an optimized configuration and keep it low going forward.
These topics will be covered
- Reducing license cost by finding and fixing misconfigurations and superfluous accounts
- How do CCB and CCX licenses really work?
- Understanding the DLAU tool and how to best utilize it
- Tips for common problem areas, like team mailboxes, functional/test users, etc
- Practical examples and best practices to implement right away
Presentation of the OECD Artificial Intelligence Review of Germany
Consult the full report at https://www.oecd.org/digital/oecd-artificial-intelligence-review-of-germany-609808d6-en.htm
Columbus Data & Analytics Wednesdays - June 2024
Columbus Data & Analytics Wednesdays, June 2024 with Maria Copot 20
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdf
Discover how MongoDB Atlas and vector search technology can revolutionize your application's search capabilities. This comprehensive presentation covers:
* What is Vector Search?
* Importance and benefits of vector search
* Practical use cases across various industries
* Step-by-step implementation guide
* Live demos with code snippets
* Enhancing LLM capabilities with vector search
* Best practices and optimization strategies
Perfect for developers, AI enthusiasts, and tech leaders. Learn how to leverage MongoDB Atlas to deliver highly relevant, context-aware search results, transforming your data retrieval process. Stay ahead in tech innovation and maximize the potential of your applications.
#MongoDB #VectorSearch #AI #SemanticSearch #TechInnovation #DataScience #LLM #MachineLearning #SearchTechnology
Taking AI to the Next Level in Manufacturing.pdf
Read Taking AI to the Next Level in Manufacturing to gain insights on AI adoption in the manufacturing industry, such as:
1. How quickly AI is being implemented in manufacturing.
2. Which barriers stand in the way of AI adoption.
3. How data quality and governance form the backbone of AI.
4. Organizational processes and structures that may inhibit effective AI adoption.
6. Ideas and approaches to help build your organization's AI strategy.
TrustArc Webinar - 2024 Global Privacy Survey
How does your privacy program stack up against your peers? What challenges are privacy teams tackling and prioritizing in 2024?
In the fifth annual Global Privacy Benchmarks Survey, we asked over 1,800 global privacy professionals and business executives to share their perspectives on the current state of privacy inside and outside of their organizations. This year’s report focused on emerging areas of importance for privacy and compliance professionals, including considerations and implications of Artificial Intelligence (AI) technologies, building brand trust, and different approaches for achieving higher privacy competence scores.
See how organizational priorities and strategic approaches to data security and privacy are evolving around the globe.
This webinar will review:
- The top 10 privacy insights from the fifth annual Global Privacy Benchmarks Survey
- The top challenges for privacy leaders, practitioners, and organizations in 2024
- Key themes to consider in developing and maintaining your privacy program
June Patch Tuesday
Ivanti’s Patch Tuesday breakdown goes beyond patching your applications and brings you the intelligence and guidance needed to prioritize where to focus your attention first. Catch early analysis on our Ivanti blog, then join industry expert Chris Goettl for the Patch Tuesday Webinar Event. There we’ll do a deep dive into each of the bulletins and give guidance on the risks associated with the newly-identified vulnerabilities.
UiPath Test Automation using UiPath Test Suite series, part 6
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
20240607 QFM018 Elixir Reading List May 2024
Everything I found interesting about the Elixir programming ecosystem in May 2024
Monitoring and Managing Anomaly Detection on OpenShift.pdf
Monitoring and Managing Anomaly Detection on OpenShift
Overview
Dive into the world of anomaly detection on edge devices with our comprehensive hands-on tutorial. This SlideShare presentation will guide you through the entire process, from data collection and model training to edge deployment and real-time monitoring. Perfect for those looking to implement robust anomaly detection systems on resource-constrained IoT/edge devices.
Key Topics Covered
1. Introduction to Anomaly Detection
- Understand the fundamentals of anomaly detection and its importance in identifying unusual behavior or failures in systems.
2. Understanding Edge (IoT)
- Learn about edge computing and IoT, and how they enable real-time data processing and decision-making at the source.
3. What is ArgoCD?
- Discover ArgoCD, a declarative, GitOps continuous delivery tool for Kubernetes, and its role in deploying applications on edge devices.
4. Deployment Using ArgoCD for Edge Devices
- Step-by-step guide on deploying anomaly detection models on edge devices using ArgoCD.
5. Introduction to Apache Kafka and S3
- Explore Apache Kafka for real-time data streaming and Amazon S3 for scalable storage solutions.
6. Viewing Kafka Messages in the Data Lake
- Learn how to view and analyze Kafka messages stored in a data lake for better insights.
7. What is Prometheus?
- Get to know Prometheus, an open-source monitoring and alerting toolkit, and its application in monitoring edge devices.
8. Monitoring Application Metrics with Prometheus
- Detailed instructions on setting up Prometheus to monitor the performance and health of your anomaly detection system.
9. What is Camel K?
- Introduction to Camel K, a lightweight integration framework built on Apache Camel, designed for Kubernetes.
10. Configuring Camel K Integrations for Data Pipelines
- Learn how to configure Camel K for seamless data pipeline integrations in your anomaly detection workflow.
11. What is a Jupyter Notebook?
- Overview of Jupyter Notebooks, an open-source web application for creating and sharing documents with live code, equations, visualizations, and narrative text.
12. Jupyter Notebooks with Code Examples
- Hands-on examples and code snippets in Jupyter Notebooks to help you implement and test anomaly detection models.
“I’m still / I’m still / Chaining from the Block”
“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.
20240605 QFM017 Machine Intelligence Reading List May 2024
Everything I found interesting about machines behaving intelligently during May 2024
Things to Consider When Choosing a Website Developer for your Website | FODUU
Choosing the right website developer is crucial for your business. This article covers essential factors to consider, including experience, portfolio, technical skills, communication, pricing, reputation & reviews, cost and budget considerations and post-launch support. Make an informed decision to ensure your website meets your business goals.
OpenID AuthZEN Interop Read Out - Authorization
During Identiverse 2024 and EIC 2024, members of the OpenID AuthZEN WG got together and demoed their authorization endpoints conforming to the AuthZEN API
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slack
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.
Recently uploaded (20)
GraphRAG for Life Science to increase LLM accuracy
GraphRAG for Life Science to increase LLM accuracy
How to Get CNIC Information System with Paksim Ga.pptx
How to Get CNIC Information System with Paksim Ga.pptx
Programming Foundation Models with DSPy - Meetup Slides
Programming Foundation Models with DSPy - Meetup Slides
みなさんこんにちはこれ何文字まで入るの？40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの？えこ...
みなさんこんにちはこれ何文字まで入るの？40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの？えこ...
HCL Notes and Domino License Cost Reduction in the World of DLAU
HCL Notes and Domino License Cost Reduction in the World of DLAU
Presentation of the OECD Artificial Intelligence Review of Germany
Presentation of the OECD Artificial Intelligence Review of Germany
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdf
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdf
UiPath Test Automation using UiPath Test Suite series, part 6
UiPath Test Automation using UiPath Test Suite series, part 6
Monitoring and Managing Anomaly Detection on OpenShift.pdf
Monitoring and Managing Anomaly Detection on OpenShift.pdf
20240605 QFM017 Machine Intelligence Reading List May 2024
20240605 QFM017 Machine Intelligence Reading List May 2024
Things to Consider When Choosing a Website Developer for your Website | FODUU
Things to Consider When Choosing a Website Developer for your Website | FODUU
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slack
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slack
Notes 7-7-8
- 1. SECTION 7-7-8 Designing Simulations with Technology
- 2. WARM-UP Go to the wiki and click on the “Stick or Switch” link under Sections 7-7 and 7-8. Use this virtual manipulative to complete the in-class activity on page 464. What strategy should the contestant use? Stick? Or Switch?
- 3. Simulation:
- 4. Simulation: An experiment that models a situation while considering all possible outcomes and situations
- 5. Simulation: An experiment that models a situation while considering all possible outcomes and situations Monte Carlo Method:
- 6. Simulation: An experiment that models a situation while considering all possible outcomes and situations Monte Carlo Method: A method that utilizes relative frequencies from repeated trials