Bayesian Statistics for Beginners

2019

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Author: Therese M. Donovan
Publisher: Oxford University Press, USA
ISBN: 0198841299
Category : Mathematics
Languages : en
Pages : 430

This is an entry-level book on Bayesian statistics written in a casual, and conversational tone. The authors walk a reader through many sample problems step-by-step to provide those with little background in math or statistics with the vocabulary, notation, and understanding of the calculations used in many Bayesian problems.

Statistics Crash Course for Beginners

2020-11-11

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Author: Ai Publishing
Publisher:
ISBN: 9781734790160
Category :
Languages : en
Pages : 330

Frequentist and Bayesian Statistics Crash Course for Beginners Data and statistics are the core subjects of Machine Learning (ML). The reality is the average programmer may be tempted to view statistics with disinterest. But if you want to exploit the incredible power of Machine Learning, you need a thorough understanding of statistics. The reason is a Machine Learning professional develops intelligent and fast algorithms that learn from data. Frequentist and Bayesian Statistics Crash Course for Beginners presents you with an easy way of learning statistics fast. Contrary to popular belief, statistics is no longer the exclusive domain of math Ph.D.s. It's true that statistics deals with numbers and percentages. Hence, the subject can be very dry and boring. This book, however, transforms statistics into a fun subject. Frequentist and Bayesian statistics are two statistical techniques that interpret the concept of probability in different ways. Bayesian statistics was first introduced by Thomas Bayes in the 1770s. Bayesian statistics has been instrumental in the design of high-end algorithms that make accurate predictions. So even after 250 years, the interest in Bayesian statistics has not faded. In fact, it has accelerated tremendously. Frequentist Statistics is just as important as Bayesian Statistics. In the statistical universe, Frequentist Statistics is the most popular inferential technique. In fact, it's the first school of thought you come across when you enter the statistics world. How Is This Book Different? AI Publishing is completely sold on the learning by doing methodology. We have gone to great lengths to ensure you find learning statistics easy. The result: you will not get stuck along your learning journey. This is not a book full of complex mathematical concepts and difficult equations. You will find that the coverage of the theoretical aspects of statistics is proportionate to the practical aspects of the subject. The book makes the reading process easier by presenting you with three types of box-tags in different colors. They are: Requirements, Further Readings, and Hands-on Time. The final chapter presents two mini-projects to give you a better understanding of the concepts you studied in the previous eight chapters. The main feature is you get instant access to a treasure trove of all the related learning material when you buy this book. They include PDFs, Python codes, exercises, and references--on the publisher's website. You get access to all this learning material at no extra cost. You can also download the Machine Learning datasets used in this book at runtime. Alternatively, you can access them through the Resources/Datasets folder. The quick course on Python programming in the first chapter will be immensely helpful, especially if you are new to Python. Since you can access all the Python codes and datasets, a computer with the internet is sufficient to get started. The topics covered include: A Quick Introduction to Python for Statistics Starting with Probability Random Variables and Probability Distributions Descriptive Statistics: Measure of Central Tendency and Spread Exploratory Analysis: Data Visualization Statistical Inference Frequentist Inference Bayesian Inference Hands-on Projects Click the BUY NOW button and start your Statistics Learning journey.

Doing Bayesian Data Analysis

2010-11-25

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Author: John Kruschke
Publisher: Academic Press
ISBN: 0123814863
Category : Mathematics
Languages : en
Pages : 673

There is an explosion of interest in Bayesian statistics, primarily because recently created computational methods have finally made Bayesian analysis tractable and accessible to a wide audience. Doing Bayesian Data Analysis, A Tutorial Introduction with R and BUGS, is for first year graduate students or advanced undergraduates and provides an accessible approach, as all mathematics is explained intuitively and with concrete examples. It assumes only algebra and ‘rusty’ calculus. Unlike other textbooks, this book begins with the basics, including essential concepts of probability and random sampling. The book gradually climbs all the way to advanced hierarchical modeling methods for realistic data. The text provides complete examples with the R programming language and BUGS software (both freeware), and begins with basic programming examples, working up gradually to complete programs for complex analyses and presentation graphics. These templates can be easily adapted for a large variety of students and their own research needs.The textbook bridges the students from their undergraduate training into modern Bayesian methods. Accessible, including the basics of essential concepts of probability and random sampling Examples with R programming language and BUGS software Comprehensive coverage of all scenarios addressed by non-bayesian textbooks- t-tests, analysis of variance (ANOVA) and comparisons in ANOVA, multiple regression, and chi-square (contingency table analysis). Coverage of experiment planning R and BUGS computer programming code on website Exercises have explicit purposes and guidelines for accomplishment

Bayes' Theorem Examples

2016-10-02

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Author: Dan Morris
Publisher: Independently Published
ISBN: 9781549761744
Category : Bayesian statistical decision theory
Languages : en
Pages : 112

***** #1 Kindle Store Bestseller in Mathematics (Throughout 2016) ********** #1 Kindle Store Bestseller in Education Theory (Throughout 2017) *****If you are looking for a short beginners guide packed with visual examples, this book is for you. Bayes' Theorem Examples: A Beginners Visual Approach to Bayesian Data Analysis If you've recently used Google search to find something, Bayes' Theorem was used to find your search results. The same is true for those recommendations on Netflix. Hedge funds? Self-driving cars? Search and Rescue? Bayes' Theorem is used in all of the above and more. At its core, Bayes' Theorem is a simple probability and statistics formula that has revolutionized how we understand and deal with uncertainty. If life is seen as black and white, Bayes' Theorem helps us think about the gray areas. When new evidence comes our way, it helps us update our beliefs and create a new belief.Ready to dig in and visually explore Bayes' Theorem? Let's go! Over 60 hand-drawn visuals are included throughout the book to help you work through each problem as you learn by example. The beautifully hand-drawn visual illustrations are specifically designed and formatted for the kindle.This book also includes sections not found in other books on Bayes' Rule. These include: A short tutorial on how to understand problem scenarios and find P(B), P(A), and P(B|A). - For many people, knowing how to approach scenarios and break them apart can be daunting. In this booklet, we provide a quick step-by-step reference on how to confidently understand scenarios. A few examples of how to think like a Bayesian in everyday life. Bayes' Rule might seem somewhat abstract, but it can be applied to many areas of life and help you make better decisions. Learn how Bayes can help you with critical thinking, problem-solving, and dealing with the gray areas of life. A concise history of Bayes' Rule. - Bayes' Theorem has a fascinating 200+ year history, and we have summed it up for you in this booklet. From its discovery in the 1700's to its being used to break the German's Enigma Code during World War 2. Fascinating real-life stories on how Bayes' formula is used everyday.From search and rescue to spam filtering and driverless cars, Bayes is used in many areas of modern day life. An expanded Bayes' Theorem definition, including notations, and proof section. - In this section we define core elementary bayesian statistics terms more concretely. A recommended readings sectionFrom The Theory That Would Not Die to Think Bayes: Bayesian Statistics in Pythoni> and many more, there are a number of fantastic resources we have collected for further reading. If you are a visual learner and like to learn by example, this intuitive Bayes' Theorem 'for dummies' type book is a good fit for you. Praise for Bayes' Theorem Examples "...What Morris has presented is a useful way to provide the reader with a basic understanding of how to apply the theorem. He takes it easy step by easy step and explains matters in a way that almost anyone can understand. Moreover, by using Venn Diagrams and other visuals, he gives the reader multiple ways of understanding exactly what is going on in Bayes' theorem. The way in which he presents this material helps solidify in the reader's mind how to use Bayes' theorem..." - Doug E. - TOP 100 REVIEWER"...For those who are predominately "Visual Learners", as I certainly am, I highly recommend this book...I believe I gained more from this book than I did from college statistics. Or at least, one fantastic refresher after 20 some years after the fact." - Tin F. TOP 50 REVIEWER

Introduction to Bayesian Statistics

2016-09-02

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Author: William M. Bolstad
Publisher: John Wiley & Sons
ISBN: 1118593227
Category : Mathematics
Languages : en
Pages : 805

"...this edition is useful and effective in teaching Bayesian inference at both elementary and intermediate levels. It is a well-written book on elementary Bayesian inference, and the material is easily accessible. It is both concise and timely, and provides a good collection of overviews and reviews of important tools used in Bayesian statistical methods." There is a strong upsurge in the use of Bayesian methods in applied statistical analysis, yet most introductory statistics texts only present frequentist methods. Bayesian statistics has many important advantages that students should learn about if they are going into fields where statistics will be used. In this third Edition, four newly-added chapters address topics that reflect the rapid advances in the field of Bayesian statistics. The authors continue to provide a Bayesian treatment of introductory statistical topics, such as scientific data gathering, discrete random variables, robust Bayesian methods, and Bayesian approaches to inference for discrete random variables, binomial proportions, Poisson, and normal means, and simple linear regression. In addition, more advanced topics in the field are presented in four new chapters: Bayesian inference for a normal with unknown mean and variance; Bayesian inference for a Multivariate Normal mean vector; Bayesian inference for the Multiple Linear Regression Model; and Computational Bayesian Statistics including Markov Chain Monte Carlo. The inclusion of these topics will facilitate readers' ability to advance from a minimal understanding of Statistics to the ability to tackle topics in more applied, advanced level books. Minitab macros and R functions are available on the book's related website to assist with chapter exercises. Introduction to Bayesian Statistics, Third Edition also features: Topics including the Joint Likelihood function and inference using independent Jeffreys priors and join conjugate prior The cutting-edge topic of computational Bayesian Statistics in a new chapter, with a unique focus on Markov Chain Monte Carlo methods Exercises throughout the book that have been updated to reflect new applications and the latest software applications Detailed appendices that guide readers through the use of R and Minitab software for Bayesian analysis and Monte Carlo simulations, with all related macros available on the book's website Introduction to Bayesian Statistics, Third Edition is a textbook for upper-undergraduate or first-year graduate level courses on introductory statistics course with a Bayesian emphasis. It can also be used as a reference work for statisticians who require a working knowledge of Bayesian statistics.

Bayes Theorem

2019-07-16

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Author: Arthur Taff
Publisher:
ISBN: 9781925997583
Category : Computers
Languages : en
Pages : 78

The Perfect Book for Beginners Wanting to Visually Learn About Bayes Theorem Through Real Examples! What if you could quickly and easily learn Bayesian data analysis without complex textbooks and statistics classes? Imagine being able to apply your newly learned theory to real life situations! Multi-time best selling IT & mathematics author, Arthur Taff, presents the perfect guide for any beginner. Bayesian data analysis can be difficult to learn, especially through textbooks and statistic classes at school. This book aims to solve that issue by presenting the theories in an easy-to-understand and visually intuitive way. This book contains a number of visual examples to build a basic understanding of Bayesian data analysis and then works to teach at a deeper level without the complexities you'd see in other similar books. Additionally, every example in this book has been solved using Excel. In this book, you will get: A Basic Introduction to Bayes Theorem (with examples) - The initial introduction demonstrates how Bayesian data analysis works when you have a single new piece of data to update initial probabilities. Adding New Data & Updating Probabilities - Takes the above example and looks at what happens if we have multiple pieces of data instead of a single piece. Bayes Theorem Terminology - The formal names for the different parts of the Bayes Theorem equation, and how it all comes together for an easier overall understanding. How to Deal With Data Errors - In a real life situation, it is unlikely that your data will be error-free. This section shows you how to deal with those errors and still get accurate probability estimates. Arthur's personal email address for unlimited customer support if you have any questions And much, much more... If you are a person that learns by example, especially visually, then this book is perfect for you! It is a very important topic in a wide range of industries - so dive in to get a deep understanding! Well, what are you waiting for? Grab your copy today by clicking the BUY NOW button at the top of this page!

A Student’s Guide to Bayesian Statistics

2018-04-20

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Author: Ben Lambert
Publisher: SAGE
ISBN: 1526418266
Category : Social Science
Languages : en
Pages : 744

Supported by a wealth of learning features, exercises, and visual elements as well as online video tutorials and interactive simulations, this book is the first student-focused introduction to Bayesian statistics. Without sacrificing technical integrity for the sake of simplicity, the author draws upon accessible, student-friendly language to provide approachable instruction perfectly aimed at statistics and Bayesian newcomers. Through a logical structure that introduces and builds upon key concepts in a gradual way and slowly acclimatizes students to using R and Stan software, the book covers: An introduction to probability and Bayesian inference Understanding Bayes′ rule Nuts and bolts of Bayesian analytic methods Computational Bayes and real-world Bayesian analysis Regression analysis and hierarchical methods This unique guide will help students develop the statistical confidence and skills to put the Bayesian formula into practice, from the basic concepts of statistical inference to complex applications of analyses.

Bayes' Rule

2013-06-01

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Author: James V. Stone
Publisher: Sebtel Press
ISBN: 0956372848
Category : Mathematics
Languages : en
Pages : 170

In this richly illustrated book, a range of accessible examples are used to show how Bayes' rule is actually a natural consequence of commonsense reasoning. The tutorial style of writing, combined with a comprehensive glossary, makes this an ideal primer for the novice who wishes to become familiar with the basic principles of Bayesian analysis.

Workshop Statistics

2001-03-01

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Author: James H. Albert
Publisher: Springer Science & Business Media
ISBN: 9781930190122
Category : Mathematics
Languages : en
Pages : 572

This first edition focuses on probability and the Bayesian viewpoint. It presents basic material on probability and then introduces inference by means of Bayes' rule. The emphasis is on statistical thinking and how one learns from data. The objective is to present the basic tenets of statistical inference. Unique in its format, the text allows students to discover statistical concepts, explore statistical principles, and apply statistical techniques. In addition to the numerous activities and exercises around which the text is built, the book includes a basic text exposition for each topic, and data appendices.

An Introduction to Bayesian Analysis

2007-07-03

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Author: Jayanta K. Ghosh
Publisher: Springer Science & Business Media
ISBN: 0387354336
Category : Mathematics
Languages : en
Pages : 356

This is a graduate-level textbook on Bayesian analysis blending modern Bayesian theory, methods, and applications. Starting from basic statistics, undergraduate calculus and linear algebra, ideas of both subjective and objective Bayesian analysis are developed to a level where real-life data can be analyzed using the current techniques of statistical computing. Advances in both low-dimensional and high-dimensional problems are covered, as well as important topics such as empirical Bayes and hierarchical Bayes methods and Markov chain Monte Carlo (MCMC) techniques. Many topics are at the cutting edge of statistical research. Solutions to common inference problems appear throughout the text along with discussion of what prior to choose. There is a discussion of elicitation of a subjective prior as well as the motivation, applicability, and limitations of objective priors. By way of important applications the book presents microarrays, nonparametric regression via wavelets as well as DMA mixtures of normals, and spatial analysis with illustrations using simulated and real data. Theoretical topics at the cutting edge include high-dimensional model selection and Intrinsic Bayes Factors, which the authors have successfully applied to geological mapping. The style is informal but clear. Asymptotics is used to supplement simulation or understand some aspects of the posterior.