Baise theorm.pptx
•Download as PPTX, PDF•
0 likes•5 views
Bayes' theorem is a statistical formula used to determine conditional probabilities. It describes the probability of an event based on prior knowledge of other events that have occurred. The formula calculates the probability of one event occurring given that another event has occurred. An example demonstrates using Bayes' theorem to calculate the probability that a ball drawn from an urn is from the first urn, given that the ball is red. The document discusses applications of Bayes' theorem in fields like marketing, medicine, puzzles, and decision analysis.
Report
Share
Report
Share
Recommended
Probability Theory for Data Scientists
This document discusses probability concepts for data science. It begins by defining probability and statistics, then covers key terms like events, random variables, empirical and theoretical probability, joint and conditional probability, probability distributions, and the central limit theorem. Examples are provided to illustrate concepts like independent and mutually exclusive events. Genetic algorithms are also introduced as a case study, outlining the phases of initializing a population, fitness functions, selection, crossover and mutation.
Phylogenetics2
This document outlines a phylogenetics workshop covering model-based tree building methods. Part II focuses on maximum likelihood and Bayesian trees. Maximum likelihood finds the tree that best fits the data under an evolutionary model by optimizing branch lengths. Bayesian trees treat trees probabilistically and integrate over tree space to find a collection of good trees and estimate confidence in relationships. Priors represent initial assumptions, while posteriors incorporate data to update beliefs about trees and parameters.
BASIC PROBABILITY distribution - Copy.pptx
This document provides an overview of key probability concepts needed for statistical inference. It defines probability as relating to outcomes that are not absolutely certain but affect the likelihood of events. Probability is quantified using experiments with sample spaces and outcomes, where classical probability assigns equal likelihood and empirical probability is based on observations. Events are defined which can be mutually exclusive, unions, or intersections. Conditional probability expresses the likelihood of one event given another. Counting rules like permutations and combinations are introduced. Random variables are defined as taking on values from a probability distribution, which can be discrete or continuous. Examples demonstrate constructing probability distributions for discrete random variables.
probability and its functions with purpose in the world's situation .pptx
probability and its functions with different context in understanding the purpose of probability in the daily life. it includes the type of probabilities and the functions. Additionally, the components and identities of the importance of relation to the concept of the world. sampling just like random, systematic, stratified sampling.
Is ignorance bliss
It is argued that when it comes to nuisance parameters an assumption of ignorance is harmful. On the other hand this raises problems as to how far one should go in searching for further data when combining evidence.
unit 3 -ML.pptx
This document provides an introduction to probabilistic and stochastic models in machine learning. It discusses key concepts like probabilistic modeling, importance of probabilistic ML models, Bayesian inference, basics of probability theory, Bayes' rule, and examples of how to apply Bayes' theorem in machine learning. The document covers conditional probability, prior and posterior probability, and how a Naive Bayes classifier uses Bayes' theorem for classification tasks.
Discrete PDs(1).pdf
This document discusses different types of discrete probability distributions, including binomial, hypergeometric, and Poisson distributions. It provides examples and formulas for each. The binomial distribution describes probability of success/failure outcomes from repeated independent trials. The hypergeometric deals with sampling without replacement. The Poisson distribution applies when the number of rare events occurs over an interval of time or space and the mean rate is known.
Bayes' theorem
This presentation gives a brief introduction about Bayes' Theorem and its applications in Artificial intelligence
Recommended
Probability Theory for Data Scientists
This document discusses probability concepts for data science. It begins by defining probability and statistics, then covers key terms like events, random variables, empirical and theoretical probability, joint and conditional probability, probability distributions, and the central limit theorem. Examples are provided to illustrate concepts like independent and mutually exclusive events. Genetic algorithms are also introduced as a case study, outlining the phases of initializing a population, fitness functions, selection, crossover and mutation.
Phylogenetics2
This document outlines a phylogenetics workshop covering model-based tree building methods. Part II focuses on maximum likelihood and Bayesian trees. Maximum likelihood finds the tree that best fits the data under an evolutionary model by optimizing branch lengths. Bayesian trees treat trees probabilistically and integrate over tree space to find a collection of good trees and estimate confidence in relationships. Priors represent initial assumptions, while posteriors incorporate data to update beliefs about trees and parameters.
BASIC PROBABILITY distribution - Copy.pptx
This document provides an overview of key probability concepts needed for statistical inference. It defines probability as relating to outcomes that are not absolutely certain but affect the likelihood of events. Probability is quantified using experiments with sample spaces and outcomes, where classical probability assigns equal likelihood and empirical probability is based on observations. Events are defined which can be mutually exclusive, unions, or intersections. Conditional probability expresses the likelihood of one event given another. Counting rules like permutations and combinations are introduced. Random variables are defined as taking on values from a probability distribution, which can be discrete or continuous. Examples demonstrate constructing probability distributions for discrete random variables.
probability and its functions with purpose in the world's situation .pptx
probability and its functions with different context in understanding the purpose of probability in the daily life. it includes the type of probabilities and the functions. Additionally, the components and identities of the importance of relation to the concept of the world. sampling just like random, systematic, stratified sampling.
Is ignorance bliss
It is argued that when it comes to nuisance parameters an assumption of ignorance is harmful. On the other hand this raises problems as to how far one should go in searching for further data when combining evidence.
unit 3 -ML.pptx
This document provides an introduction to probabilistic and stochastic models in machine learning. It discusses key concepts like probabilistic modeling, importance of probabilistic ML models, Bayesian inference, basics of probability theory, Bayes' rule, and examples of how to apply Bayes' theorem in machine learning. The document covers conditional probability, prior and posterior probability, and how a Naive Bayes classifier uses Bayes' theorem for classification tasks.
Discrete PDs(1).pdf
This document discusses different types of discrete probability distributions, including binomial, hypergeometric, and Poisson distributions. It provides examples and formulas for each. The binomial distribution describes probability of success/failure outcomes from repeated independent trials. The hypergeometric deals with sampling without replacement. The Poisson distribution applies when the number of rare events occurs over an interval of time or space and the mean rate is known.
Bayes' theorem
This presentation gives a brief introduction about Bayes' Theorem and its applications in Artificial intelligence
Bayesian learning
This document discusses Bayesian learning and the Bayes theorem. Some key points:
- Bayesian learning uses probabilities to calculate the likelihood of hypotheses given observed data and prior probabilities. The naive Bayes classifier is an example.
- The Bayes theorem provides a way to calculate the posterior probability of a hypothesis given observed training data by considering the prior probability and likelihood of the data under the hypothesis.
- Bayesian methods can incorporate prior knowledge and probabilistic predictions, and classify new instances by combining predictions from multiple hypotheses weighted by their probabilities.
Chapter 1 2021.pdf
This document provides an overview of a probability and statistics course, including the textbook, topics covered, evaluation scheme, and sample chapter content. The course covers topics such as descriptive statistics, probability theory, random variables, sampling distributions, and inferential statistics. Evaluation is based on a midterm exam, report, activities, and final exam. The sample chapter discusses descriptive statistics, data types, measures of central tendency (mean, median, mode), measures of dispersion (range, variance, standard deviation), and data representation methods.
Math138 lectures 3rd edition scoolbook
This document provides information about a course on quantitative techniques taught by Edwin S. Bunag. It includes his contact information and educational background. The course description states it will cover using probabilities, inventory, forecasting, linear programming, and other operations research techniques to analyze business decision making. The course outline lists various topics that will be covered, including probability, decision analysis, linear programming, and simulation. An overview section discusses the need for managers to be good decision makers and defines problem solving and the decision making process. It also provides background on management science, operations research, and quantitative techniques.
Chapter 1 2022.pdf
This document provides an overview of a probability and statistics course, including the textbook, topics covered, evaluation scheme, and sample chapter content. The course topics include probability theory, random variables, distributions, and statistical methods. Evaluation is based on a midterm, report, activities, and final exam. The sample chapter discusses descriptive statistics, data collection and analysis, and graphical methods like histograms, bar charts, and scatter plots. It also defines measures of central tendency and dispersion.
Artificial Intelligence Notes Unit 3
This document discusses handling uncertainty through probabilistic reasoning and machine learning techniques. It covers sources of uncertainty like incomplete data, probabilistic effects, and uncertain outputs from inference. Approaches covered include Bayesian networks, Bayes' theorem, conditional probability, joint probability distributions, and Dempster-Shafer theory. It provides examples of calculating conditional probabilities and using Bayes' theorem. Bayesian networks are defined as directed acyclic graphs representing probabilistic dependencies between variables, and examples show how to represent domains of uncertainty and perform probabilistic reasoning using a Bayesian network.
Testing of hypothesis and Goodness of fit
Testing of Hypothesis and Goodness of Fit
This document discusses hypothesis testing and goodness of fit. It defines hypothesis testing as a procedure to determine if sample data agrees with a hypothesized population characteristic. The key steps are stating the null and alternative hypotheses, selecting a significance level, determining the test distribution, defining rejection regions, performing the statistical test, and drawing a conclusion. Common hypothesis tests discussed include the Student's t-test and chi-square test of goodness of fit.
STSTISTICS AND PROBABILITY THEORY .pptx
The document discusses key concepts in probability theory including probability, random experiments, sample spaces, events, random variables, probability distributions, and Bayes' theorem. It covers the binomial, Poisson, and normal distributions and their characteristics and applications. Decision theory is introduced as analyzing choices under uncertainty involving defining problems, identifying outcomes, assessing criteria, and evaluating alternatives to make optimal decisions.
Probability and Statistics - Week 1
This document provides an overview of a probability and statistics course, including the grading criteria, topics that will be covered like machine learning, probability in real life examples, key terminology, types of events, and how probability is used in programming. The course will cover sample space, events, counting sample points, and probability of an event. Assignments will make up 20% of the grade, with the midterm and final exam making up 30% and 40% respectively. Topics that will be discussed include random variables, empirical vs theoretical probability, independent and mutually exclusive events, and probability distributions. Examples are provided for calculating probabilities of different events.
Whatever happened to design based inference
Given as the Sprott lecture, University of Waterloo September 2022
Abstract
What exactly should we think about appropriate analyses for designed experiments and why? If conditional inference trumps marginal inference, why should we care about randomisation? Isn’t everything just modelling? The Rothamsted School held that design matters. Taking an example of applying John Nelder’s general balance approach to a notorious problem, Lord’s paradox, I shall show that there may be some lessons for two fashionable topics: causal analysis and big data. I shall conclude that if we want not only to make good estimates but estimate how good our estimates are, design does matter.
Test of significance
The document provides an overview of statistical hypothesis testing and various statistical tests used to analyze quantitative and qualitative data. It discusses types of data, key terms like null hypothesis and p-value. It then outlines the steps in hypothesis testing and describes different tests of significance including standard error of difference between proportions, chi-square test, student's t-test, paired t-test, and ANOVA. Examples are provided to demonstrate how to apply these statistical tests to determine if differences observed in sample data are statistically significant.
probability.pptx
This document contains lecture notes on probability concepts. It begins by reviewing the previous lecture on descriptive statistics and introducing the topics that will be covered in the current and next two lectures: experiment, event, sample space, probability, counting rules, conditional probability, Bayes' rule, random variables, mean, and variance. It then provides definitions and examples for basic probability concepts like experiments, events, mutually exclusive events, simple events, and sample spaces. It discusses how to calculate probabilities using counting rules and formulas for permutations and combinations.
Navies bayes
The document discusses Naive Bayes classifiers, which are a family of algorithms for classification that are based on Bayes' theorem and assume independence between features. It provides definitions of key terms like conditional probability and Bayes' theorem. It then derives the Naive Bayes classifier equation and discusses how it works, including an example of classifying whether to play golf based on weather conditions. The document also covers advantages like speed, disadvantages like the independence assumption, and applications like spam filtering.
Probability 23321
This document discusses various probability distributions including binomial, Poisson, and normal distributions. It provides examples of how each distribution can be used to model real-world scenarios involving probability. The binomial distribution describes experiments with two possible outcomes, like coin flips. The Poisson distribution models rare, independent events that occur randomly over time. The normal distribution approximates many natural phenomena and test scores due to the central limit theorem.
Probability 23321
This document discusses various probability distributions including binomial, Poisson, and normal distributions. It provides examples of how each distribution can be used to model real-world scenarios involving probability. The binomial distribution describes experiments with two possible outcomes, like coin flips. The Poisson distribution models rare, independent events that occur randomly over time. The normal distribution approximates many natural phenomena and test scores due to the central limit theorem.
Hypothesis testing , T test , chi square test, z test
- The document discusses hypothesis testing and the p-value approach, which involves specifying the null and alternative hypotheses, calculating a test statistic, determining the p-value, and comparing it to the significance level α to determine whether to reject or accept the null hypothesis.
- It also discusses type I and type II errors, degrees of freedom as the number of independent pieces of information, and chi-square and t-tests as statistical tests.
Geneticschapter2part2 140126121602-phpapp02
The document provides information about genetics concepts and assessments. It discusses results from homework 1 and upcoming genetics quizzes. Key points include:
- Students performed well on homework 1, scoring on average 2.9/3 and 8.9/10 on different questions. A few scores were above 10.
- Quiz 1 is open with a 30 minute time limit and 10 questions. Quiz 2 will be similar and is due February 5th. Quizzes 3-5 will have 20 questions each. No late submissions will receive credit.
- The document then covers Mendelian genetics concepts like transmission genetics, inheritance patterns, probability, and how these relate to molecular genetics and protein function. It provides examples and
The Rothamsted School & The analysis of designed experiments
A historical account is given of the approach of "The Rothamsted School" to the analysis of designed experiments. The link between the way that experiments are designed and how they should be analysed is fundamental to this approach. The key figures are RA Fisher, Frank Yates and John Nelder
4.2 binomial distributions
This document discusses binomial distributions and provides examples of determining if experiments are binomial, calculating binomial probabilities, constructing binomial distributions, and graphing binomial distributions. It defines a binomial experiment as having a fixed number of independent trials with two possible outcomes, where the probability of success is the same for each trial. Examples demonstrate how to identify n, p, q, and the possible values of x for binomial experiments. The document also shows how to use the binomial probability formula, tables, and technology to calculate probabilities.
Ch7 gene disorders and pedigree analysis
Presenting the probability in genetics; Product law, Sum law, Binomial Theorem and Chi-square. Also brief explanation on pedigree analysis and genetic disorders of autosomal dominant and recessive.
Introduction to Hypothesis Testing
This document provides an overview of hypothesis testing including:
1) The four steps of hypothesis testing - stating hypotheses, setting criteria, collecting data, and making a decision. It also discusses types of errors.
2) Factors that influence the outcome like effect size, sample size, and variability. Larger effects, samples, and less variability make rejecting the null hypothesis more likely.
3) Direction hypotheses tests where the alternative predicts a direction of the effect. This allows rejecting the null with smaller differences but in the predicted direction.
4) Effect size measures like Cohen's d provide information beyond just significance. Statistical power is the probability of correctly rejecting a false null hypothesis.
Butterfly Valves Manufacturer (LBF Series).pdf
We have designed & manufacture the Lubi Valves LBF series type of Butterfly Valves for General Utility Water applications as well as for HVAC applications.
FUNDAMENTALS OF MECHANICAL ENGINEERING.pdf
EMERSON EDUARDO RODRIGUES
EMERSON EDUARDO RODRIGUES
EMERSON EDUARDO RODRIGUES
EMERSON EDUARDO RODRIGUES
EMERSON EDUARDO RODRIGUES
EMERSON EDUARDO RODRIGUES
EMERSON EDUARDO RODRIGUES
EMERSON EDUARDO RODRIGUES
EMERSON EDUARDO RODRIGUES
EMERSON EDUARDO RODRIGUES
EMERSON EDUARDO RODRIGUES
More Related Content
Similar to Baise theorm.pptx
Bayesian learning
This document discusses Bayesian learning and the Bayes theorem. Some key points:
- Bayesian learning uses probabilities to calculate the likelihood of hypotheses given observed data and prior probabilities. The naive Bayes classifier is an example.
- The Bayes theorem provides a way to calculate the posterior probability of a hypothesis given observed training data by considering the prior probability and likelihood of the data under the hypothesis.
- Bayesian methods can incorporate prior knowledge and probabilistic predictions, and classify new instances by combining predictions from multiple hypotheses weighted by their probabilities.
Chapter 1 2021.pdf
This document provides an overview of a probability and statistics course, including the textbook, topics covered, evaluation scheme, and sample chapter content. The course covers topics such as descriptive statistics, probability theory, random variables, sampling distributions, and inferential statistics. Evaluation is based on a midterm exam, report, activities, and final exam. The sample chapter discusses descriptive statistics, data types, measures of central tendency (mean, median, mode), measures of dispersion (range, variance, standard deviation), and data representation methods.
Math138 lectures 3rd edition scoolbook
This document provides information about a course on quantitative techniques taught by Edwin S. Bunag. It includes his contact information and educational background. The course description states it will cover using probabilities, inventory, forecasting, linear programming, and other operations research techniques to analyze business decision making. The course outline lists various topics that will be covered, including probability, decision analysis, linear programming, and simulation. An overview section discusses the need for managers to be good decision makers and defines problem solving and the decision making process. It also provides background on management science, operations research, and quantitative techniques.
Chapter 1 2022.pdf
This document provides an overview of a probability and statistics course, including the textbook, topics covered, evaluation scheme, and sample chapter content. The course topics include probability theory, random variables, distributions, and statistical methods. Evaluation is based on a midterm, report, activities, and final exam. The sample chapter discusses descriptive statistics, data collection and analysis, and graphical methods like histograms, bar charts, and scatter plots. It also defines measures of central tendency and dispersion.
Artificial Intelligence Notes Unit 3
This document discusses handling uncertainty through probabilistic reasoning and machine learning techniques. It covers sources of uncertainty like incomplete data, probabilistic effects, and uncertain outputs from inference. Approaches covered include Bayesian networks, Bayes' theorem, conditional probability, joint probability distributions, and Dempster-Shafer theory. It provides examples of calculating conditional probabilities and using Bayes' theorem. Bayesian networks are defined as directed acyclic graphs representing probabilistic dependencies between variables, and examples show how to represent domains of uncertainty and perform probabilistic reasoning using a Bayesian network.
Testing of hypothesis and Goodness of fit
Testing of Hypothesis and Goodness of Fit
This document discusses hypothesis testing and goodness of fit. It defines hypothesis testing as a procedure to determine if sample data agrees with a hypothesized population characteristic. The key steps are stating the null and alternative hypotheses, selecting a significance level, determining the test distribution, defining rejection regions, performing the statistical test, and drawing a conclusion. Common hypothesis tests discussed include the Student's t-test and chi-square test of goodness of fit.
STSTISTICS AND PROBABILITY THEORY .pptx
The document discusses key concepts in probability theory including probability, random experiments, sample spaces, events, random variables, probability distributions, and Bayes' theorem. It covers the binomial, Poisson, and normal distributions and their characteristics and applications. Decision theory is introduced as analyzing choices under uncertainty involving defining problems, identifying outcomes, assessing criteria, and evaluating alternatives to make optimal decisions.
Probability and Statistics - Week 1
This document provides an overview of a probability and statistics course, including the grading criteria, topics that will be covered like machine learning, probability in real life examples, key terminology, types of events, and how probability is used in programming. The course will cover sample space, events, counting sample points, and probability of an event. Assignments will make up 20% of the grade, with the midterm and final exam making up 30% and 40% respectively. Topics that will be discussed include random variables, empirical vs theoretical probability, independent and mutually exclusive events, and probability distributions. Examples are provided for calculating probabilities of different events.
Whatever happened to design based inference
Given as the Sprott lecture, University of Waterloo September 2022
Abstract
What exactly should we think about appropriate analyses for designed experiments and why? If conditional inference trumps marginal inference, why should we care about randomisation? Isn’t everything just modelling? The Rothamsted School held that design matters. Taking an example of applying John Nelder’s general balance approach to a notorious problem, Lord’s paradox, I shall show that there may be some lessons for two fashionable topics: causal analysis and big data. I shall conclude that if we want not only to make good estimates but estimate how good our estimates are, design does matter.
Test of significance
The document provides an overview of statistical hypothesis testing and various statistical tests used to analyze quantitative and qualitative data. It discusses types of data, key terms like null hypothesis and p-value. It then outlines the steps in hypothesis testing and describes different tests of significance including standard error of difference between proportions, chi-square test, student's t-test, paired t-test, and ANOVA. Examples are provided to demonstrate how to apply these statistical tests to determine if differences observed in sample data are statistically significant.
probability.pptx
This document contains lecture notes on probability concepts. It begins by reviewing the previous lecture on descriptive statistics and introducing the topics that will be covered in the current and next two lectures: experiment, event, sample space, probability, counting rules, conditional probability, Bayes' rule, random variables, mean, and variance. It then provides definitions and examples for basic probability concepts like experiments, events, mutually exclusive events, simple events, and sample spaces. It discusses how to calculate probabilities using counting rules and formulas for permutations and combinations.
Navies bayes
The document discusses Naive Bayes classifiers, which are a family of algorithms for classification that are based on Bayes' theorem and assume independence between features. It provides definitions of key terms like conditional probability and Bayes' theorem. It then derives the Naive Bayes classifier equation and discusses how it works, including an example of classifying whether to play golf based on weather conditions. The document also covers advantages like speed, disadvantages like the independence assumption, and applications like spam filtering.
Probability 23321
This document discusses various probability distributions including binomial, Poisson, and normal distributions. It provides examples of how each distribution can be used to model real-world scenarios involving probability. The binomial distribution describes experiments with two possible outcomes, like coin flips. The Poisson distribution models rare, independent events that occur randomly over time. The normal distribution approximates many natural phenomena and test scores due to the central limit theorem.
Probability 23321
This document discusses various probability distributions including binomial, Poisson, and normal distributions. It provides examples of how each distribution can be used to model real-world scenarios involving probability. The binomial distribution describes experiments with two possible outcomes, like coin flips. The Poisson distribution models rare, independent events that occur randomly over time. The normal distribution approximates many natural phenomena and test scores due to the central limit theorem.
Hypothesis testing , T test , chi square test, z test
- The document discusses hypothesis testing and the p-value approach, which involves specifying the null and alternative hypotheses, calculating a test statistic, determining the p-value, and comparing it to the significance level α to determine whether to reject or accept the null hypothesis.
- It also discusses type I and type II errors, degrees of freedom as the number of independent pieces of information, and chi-square and t-tests as statistical tests.
Geneticschapter2part2 140126121602-phpapp02
The document provides information about genetics concepts and assessments. It discusses results from homework 1 and upcoming genetics quizzes. Key points include:
- Students performed well on homework 1, scoring on average 2.9/3 and 8.9/10 on different questions. A few scores were above 10.
- Quiz 1 is open with a 30 minute time limit and 10 questions. Quiz 2 will be similar and is due February 5th. Quizzes 3-5 will have 20 questions each. No late submissions will receive credit.
- The document then covers Mendelian genetics concepts like transmission genetics, inheritance patterns, probability, and how these relate to molecular genetics and protein function. It provides examples and
The Rothamsted School & The analysis of designed experiments
A historical account is given of the approach of "The Rothamsted School" to the analysis of designed experiments. The link between the way that experiments are designed and how they should be analysed is fundamental to this approach. The key figures are RA Fisher, Frank Yates and John Nelder
4.2 binomial distributions
This document discusses binomial distributions and provides examples of determining if experiments are binomial, calculating binomial probabilities, constructing binomial distributions, and graphing binomial distributions. It defines a binomial experiment as having a fixed number of independent trials with two possible outcomes, where the probability of success is the same for each trial. Examples demonstrate how to identify n, p, q, and the possible values of x for binomial experiments. The document also shows how to use the binomial probability formula, tables, and technology to calculate probabilities.
Ch7 gene disorders and pedigree analysis
Presenting the probability in genetics; Product law, Sum law, Binomial Theorem and Chi-square. Also brief explanation on pedigree analysis and genetic disorders of autosomal dominant and recessive.
Introduction to Hypothesis Testing
This document provides an overview of hypothesis testing including:
1) The four steps of hypothesis testing - stating hypotheses, setting criteria, collecting data, and making a decision. It also discusses types of errors.
2) Factors that influence the outcome like effect size, sample size, and variability. Larger effects, samples, and less variability make rejecting the null hypothesis more likely.
3) Direction hypotheses tests where the alternative predicts a direction of the effect. This allows rejecting the null with smaller differences but in the predicted direction.
4) Effect size measures like Cohen's d provide information beyond just significance. Statistical power is the probability of correctly rejecting a false null hypothesis.
Similar to Baise theorm.pptx (20)
Hypothesis testing , T test , chi square test, z test
Hypothesis testing , T test , chi square test, z test
The Rothamsted School & The analysis of designed experiments
The Rothamsted School & The analysis of designed experiments
Recently uploaded
Butterfly Valves Manufacturer (LBF Series).pdf
We have designed & manufacture the Lubi Valves LBF series type of Butterfly Valves for General Utility Water applications as well as for HVAC applications.
FUNDAMENTALS OF MECHANICAL ENGINEERING.pdf
EMERSON EDUARDO RODRIGUES
EMERSON EDUARDO RODRIGUES
EMERSON EDUARDO RODRIGUES
EMERSON EDUARDO RODRIGUES
EMERSON EDUARDO RODRIGUES
EMERSON EDUARDO RODRIGUES
EMERSON EDUARDO RODRIGUES
EMERSON EDUARDO RODRIGUES
EMERSON EDUARDO RODRIGUES
EMERSON EDUARDO RODRIGUES
EMERSON EDUARDO RODRIGUES
Flow Through Pipe: the analysis of fluid flow within pipes
Flow Through Pipe: This topic covers the analysis of fluid flow within pipes, focusing on laminar and turbulent flow regimes, continuity equation, Bernoulli's equation, Darcy-Weisbach equation, head loss due to friction, and minor losses from fittings and bends. Understanding these principles is crucial for efficient pipe system design and analysis.
An In-Depth Exploration of Natural Language Processing: Evolution, Applicatio...
Natural language processing (NLP) has
recently garnered significant interest for the
computational representation and analysis of human
language. Its applications span multiple domains such
as machine translation, email spam detection,
information extraction, summarization, healthcare,
and question answering. This paper first delineates
four phases by examining various levels of NLP and
components of Natural Language Generation,
followed by a review of the history and progression of
NLP. Subsequently, we delve into the current state of
the art by presenting diverse NLP applications,
contemporary trends, and challenges. Finally, we
discuss some available datasets, models, and
evaluation metrics in NLP.
一比一原版(psu学位证书)美国匹兹堡州立大学毕业证如何办理
原版一模一样【微信：741003700 】【(psu学位证书)美国匹兹堡州立大学毕业证成绩单】【微信：741003700 】学位证，留信认证（真实可查，永久存档）原件一模一样纸张工艺/offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
本公司拥有海外各大学样板无数，能完美还原。
1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700
【主营项目】
一.毕业证【q微741003700】成绩单、使馆认证、教育部认证、雅思托福成绩单、学生卡等！
二.真实使馆公证(即留学回国人员证明,不成功不收费)
三.真实教育部学历学位认证（教育部存档！教育部留服网站永久可查）
四.办理各国各大学文凭(一对一专业服务,可全程监控跟踪进度)
如果您处于以下几种情况：
◇在校期间，因各种原因未能顺利毕业……拿不到官方毕业证【q/微741003700】
◇面对父母的压力，希望尽快拿到；
◇不清楚认证流程以及材料该如何准备；
◇回国时间很长，忘记办理；
◇回国马上就要找工作，办给用人单位看；
◇企事业单位必须要求办理的
◇需要报考公务员、购买免税车、落转户口
◇申请留学生创业基金
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
办理(psu学位证书)美国匹兹堡州立大学毕业证【微信：741003700 】外观非常简单，由纸质材料制成，上面印有校徽、校名、毕业生姓名、专业等信息。
办理(psu学位证书)美国匹兹堡州立大学毕业证【微信：741003700 】格式相对统一，各专业都有相应的模板。通常包括以下部分：
校徽：象征着学校的荣誉和传承。
校名:学校英文全称
授予学位：本部分将注明获得的具体学位名称。
毕业生姓名：这是最重要的信息之一，标志着该证书是由特定人员获得的。
颁发日期：这是毕业正式生效的时间，也代表着毕业生学业的结束。
其他信息：根据不同的专业和学位，可能会有一些特定的信息或章节。
办理(psu学位证书)美国匹兹堡州立大学毕业证【微信：741003700 】价值很高，需要妥善保管。一般来说，应放置在安全、干燥、防潮的地方，避免长时间暴露在阳光下。如需使用，最好使用复印件而不是原件，以免丢失。
综上所述，办理(psu学位证书)美国匹兹堡州立大学毕业证【微信：741003700 】是证明身份和学历的高价值文件。外观简单庄重，格式统一，包括重要的个人信息和发布日期。对持有人来说，妥善保管是非常重要的。
Object Oriented Analysis and Design - OOAD
This ppt gives detailed description of Object Oriented Analysis and design.
Blood finder application project report (1).pdf
Blood Finder is an emergency time app where a user can search for the blood banks as
well as the registered blood donors around Mumbai. This application also provide an
opportunity for the user of this application to become a registered donor for this user have
to enroll for the donor request from the application itself. If the admin wish to make user
a registered donor, with some of the formalities with the organization it can be done.
Specialization of this application is that the user will not have to register on sign-in for
searching the blood banks and blood donors it can be just done by installing the
application to the mobile.
The purpose of making this application is to save the user’s time for searching blood of
needed blood group during the time of the emergency.
This is an android application developed in Java and XML with the connectivity of
SQLite database. This application will provide most of basic functionality required for an
emergency time application. All the details of Blood banks and Blood donors are stored
in the database i.e. SQLite.
This application allowed the user to get all the information regarding blood banks and
blood donors such as Name, Number, Address, Blood Group, rather than searching it on
the different websites and wasting the precious time. This application is effective and
user friendly.
OOPS_Lab_Manual - programs using C++ programming language
This manual contains programs on object oriented programming concepts using C++ language.
Call For Paper -3rd International Conference on Artificial Intelligence Advan...
* Registration is currently open *
Call for Research Papers!!!
Free – Extended Paper will be published as free of cost.
3rd International Conference on Artificial Intelligence Advances (AIAD 2024)
July 13 ~ 14, 2024, Virtual Conference
Webpage URL: https://aiad2024.org/index
Submission Deadline: June 22, 2024
Submission System URL:
https://aiad2024.org/submission/index.php
Contact Us:
Here's where you can reach us : aiad@aiad2024.org (or) aiadconference@yahoo.com
WikiCFP URL: http://wikicfp.com/cfp/servlet/event.showcfp?eventid=180509©ownerid=171656
#artificialintelligence #softcomputing #machinelearning #technology #datascience #python #deeplearning #tech #robotics #innovation #bigdata #coding #iot #computerscience #data #dataanalytics #engineering #robot #datascientist #software #automation #analytics #ml #pythonprogramming #programmer #digitaltransformation #developer #promptengineering #generativeai #genai #chatgpt #artificial #intelligence #datamining #networkscommunications #robotics #callforsubmission #submissionsopen #deadline #opencall #virtual #conference
一比一原版(osu毕业证书)美国俄勒冈州立大学毕业证如何办理
原版一模一样【微信：741003700 】【(osu毕业证书)美国俄勒冈州立大学毕业证成绩单】【微信：741003700 】学位证，留信认证（真实可查，永久存档）原件一模一样纸张工艺/offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
本公司拥有海外各大学样板无数，能完美还原。
1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700
【主营项目】
一.毕业证【q微741003700】成绩单、使馆认证、教育部认证、雅思托福成绩单、学生卡等！
二.真实使馆公证(即留学回国人员证明,不成功不收费)
三.真实教育部学历学位认证（教育部存档！教育部留服网站永久可查）
四.办理各国各大学文凭(一对一专业服务,可全程监控跟踪进度)
如果您处于以下几种情况：
◇在校期间，因各种原因未能顺利毕业……拿不到官方毕业证【q/微741003700】
◇面对父母的压力，希望尽快拿到；
◇不清楚认证流程以及材料该如何准备；
◇回国时间很长，忘记办理；
◇回国马上就要找工作，办给用人单位看；
◇企事业单位必须要求办理的
◇需要报考公务员、购买免税车、落转户口
◇申请留学生创业基金
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
办理(osu毕业证书)美国俄勒冈州立大学毕业证【微信：741003700 】外观非常简单，由纸质材料制成，上面印有校徽、校名、毕业生姓名、专业等信息。
办理(osu毕业证书)美国俄勒冈州立大学毕业证【微信：741003700 】格式相对统一，各专业都有相应的模板。通常包括以下部分：
校徽：象征着学校的荣誉和传承。
校名:学校英文全称
授予学位：本部分将注明获得的具体学位名称。
毕业生姓名：这是最重要的信息之一，标志着该证书是由特定人员获得的。
颁发日期：这是毕业正式生效的时间，也代表着毕业生学业的结束。
其他信息：根据不同的专业和学位，可能会有一些特定的信息或章节。
办理(osu毕业证书)美国俄勒冈州立大学毕业证【微信：741003700 】价值很高，需要妥善保管。一般来说，应放置在安全、干燥、防潮的地方，避免长时间暴露在阳光下。如需使用，最好使用复印件而不是原件，以免丢失。
综上所述，办理(osu毕业证书)美国俄勒冈州立大学毕业证【微信：741003700 】是证明身份和学历的高价值文件。外观简单庄重，格式统一，包括重要的个人信息和发布日期。对持有人来说，妥善保管是非常重要的。
Digital Twins Computer Networking Paper Presentation.pptx
A Digital Twin in computer networking is a virtual representation of a physical network, used to simulate, analyze, and optimize network performance and reliability. It leverages real-time data to enhance network management, predict issues, and improve decision-making processes.
Call Girls Goa (india) ☎️ +91-7426014248 Goa Call Girl
Call Girls Goa (india) ☎️ +91-7426014248 Goa Call Girl
一比一原版(uofo毕业证书)美国俄勒冈大学毕业证如何办理
原版一模一样【微信：741003700 】【(uofo毕业证书)美国俄勒冈大学毕业证成绩单】【微信：741003700 】学位证，留信认证（真实可查，永久存档）原件一模一样纸张工艺/offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
本公司拥有海外各大学样板无数，能完美还原。
1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700
【主营项目】
一.毕业证【q微741003700】成绩单、使馆认证、教育部认证、雅思托福成绩单、学生卡等！
二.真实使馆公证(即留学回国人员证明,不成功不收费)
三.真实教育部学历学位认证（教育部存档！教育部留服网站永久可查）
四.办理各国各大学文凭(一对一专业服务,可全程监控跟踪进度)
如果您处于以下几种情况：
◇在校期间，因各种原因未能顺利毕业……拿不到官方毕业证【q/微741003700】
◇面对父母的压力，希望尽快拿到；
◇不清楚认证流程以及材料该如何准备；
◇回国时间很长，忘记办理；
◇回国马上就要找工作，办给用人单位看；
◇企事业单位必须要求办理的
◇需要报考公务员、购买免税车、落转户口
◇申请留学生创业基金
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
办理(uofo毕业证书)美国俄勒冈大学毕业证【微信：741003700 】外观非常简单，由纸质材料制成，上面印有校徽、校名、毕业生姓名、专业等信息。
办理(uofo毕业证书)美国俄勒冈大学毕业证【微信：741003700 】格式相对统一，各专业都有相应的模板。通常包括以下部分：
校徽：象征着学校的荣誉和传承。
校名:学校英文全称
授予学位：本部分将注明获得的具体学位名称。
毕业生姓名：这是最重要的信息之一，标志着该证书是由特定人员获得的。
颁发日期：这是毕业正式生效的时间，也代表着毕业生学业的结束。
其他信息：根据不同的专业和学位，可能会有一些特定的信息或章节。
办理(uofo毕业证书)美国俄勒冈大学毕业证【微信：741003700 】价值很高，需要妥善保管。一般来说，应放置在安全、干燥、防潮的地方，避免长时间暴露在阳光下。如需使用，最好使用复印件而不是原件，以免丢失。
综上所述，办理(uofo毕业证书)美国俄勒冈大学毕业证【微信：741003700 】是证明身份和学历的高价值文件。外观简单庄重，格式统一，包括重要的个人信息和发布日期。对持有人来说，妥善保管是非常重要的。
Call Girls Chennai +91-8824825030 Vip Call Girls Chennai
Call Girls Chennai +91-8824825030 Vip Call Girls Chennai
Recently uploaded (20)
Flow Through Pipe: the analysis of fluid flow within pipes
Flow Through Pipe: the analysis of fluid flow within pipes
An In-Depth Exploration of Natural Language Processing: Evolution, Applicatio...
An In-Depth Exploration of Natural Language Processing: Evolution, Applicatio...
Northrop Grumman - Aerospace Structures Overvi.pdf
Northrop Grumman - Aerospace Structures Overvi.pdf
OOPS_Lab_Manual - programs using C++ programming language
OOPS_Lab_Manual - programs using C++ programming language
Call For Paper -3rd International Conference on Artificial Intelligence Advan...
Call For Paper -3rd International Conference on Artificial Intelligence Advan...
Digital Twins Computer Networking Paper Presentation.pptx
Digital Twins Computer Networking Paper Presentation.pptx
Call Girls Goa (india) ☎️ +91-7426014248 Goa Call Girl
Call Girls Goa (india) ☎️ +91-7426014248 Goa Call Girl
SENTIMENT ANALYSIS ON PPT AND Project template_.pptx
SENTIMENT ANALYSIS ON PPT AND Project template_.pptx
Call Girls Chennai +91-8824825030 Vip Call Girls Chennai
Call Girls Chennai +91-8824825030 Vip Call Girls Chennai
Baise theorm.pptx
- 1. NUGULA SWATI 20220802311 SHUBHAM LADE 20220802312 VEDANT MORE 20220802313 Bayes’ Theorem
- 2. ANOTHER EVENT, IS EQUAL TO THE LIKELIHOO D OF THE SECOND EVENT GIVEN THE FIRST EVENT MULTIPLIE D BY THE PROBABILI TY OF THE FIRST EVENT. Introduction
- 3. Definition • Bayes theorem is a statistical formula to determine the conditional probability of an event. It describes the probability of an event based on prior knowledge of events that have already happened. Bayes Theorem is named after the Reverend Thomas Bayes . Formula • Formula of Bayes' Theorem for random variable :- Statistics
- 4. • Three urns contain 6 red, 4 black; 4 red, 6 black, and 5 red, 5 black balls respectively. One of the urns is selected at random and a ball is drawn from it. If the ball drawn is red, find the probability that it is drawn from the first urn. • Bayes Theorem can be derived for events and random variables separately using the definition of conditional probability and density. E1 = urn first is chosen E2 = urn second is chosen E3 = urn third is chosen A = ball drawn is red P(E1) = P(E2) = P(E3) = 1/3 P(A/E1) = 6/10 P(A/E2) = 4/10 P(A/E3) = 5/10 P(E1/A) = P(E1) P(A/E1) + P(E2) P(A/E2) + P(E3) P(A/E3) = (1/3 * 6/10) + (1/3 * 4/10) + (1/3 * 5/10) =2/5 Example Proof
- 5. Advantages Disadvantages • The method provides a principled and natural way in the estimation of the probability. • It is very much Flexible and prior data which are used in this method is realistic. • It is following the principle of likelihood which is helpful for the estimation of probability. • Bayes’ theorem is interpreted probability with the help of mathematical formula. • This does not have any correct or particular way which helps to choose the prior. • This method produces the posterior distribution by the influence of the prior information • Sometimes there needs to be a high compilation cost to complete the method when there are a large number of parameters or areas
- 6. Applicat ions Internet Marketing Medical Field Solving Puzzles The Two Child Problem The Monty H Problem Cancer rate Drug testing Three Prisoner Problem Decision Analysis Decision Trees
- 7. • It is concluded that this study is discussing Bayes theorem and its formula and use in everyday life. In the study, this is seen: “Bayes theorem” is a mathematical method that helps to interpret the future situation. • This theory is help to interpreted the “Probability” of one situation by Conclusion References • Chat GPT • Wikipedia • First Course in Portability