Bayesian Probability For Babies

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Bayesian Probability For Babies

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Author : Chris Ferrie
language : en
Publisher: Sourcebooks, Inc.
Release Date : 2019-07-02

Bayesian Probability For Babies written by Chris Ferrie and has been published by Sourcebooks, Inc. this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-07-02 with Juvenile Nonfiction categories.

Fans of Chris Ferrie's Rocket Science for Babies, Astrophysics for Babies, and 8 Little Planets will love this introduction to the basic principles of probability for babies and toddlers! Help your future genius become the smartest baby in the room! It only takes a small spark to ignite a child's mind. If you took a bite out of a cookie and that bite has no candy in it, what is the probability that bite came from a candy cookie or a cookie with no candy? You and baby will find out the probability and discover it through different types of distribution. Yet another Baby University board book full of simple explanations of complex ideas written by an expert for your future genius! If you're looking for baby math books, probability for kids, or more Baby University board books to surprise your little one, look no further! Bayesian Probability for Babies offers fun early learning for your little scientist!

Bayesian Probability For Babies

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Author : Chris Ferrie
language : en
Publisher: Sourcebooks Jabberwocky
Release Date : 2019-07

Bayesian Probability For Babies written by Chris Ferrie and has been published by Sourcebooks Jabberwocky this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-07 with Juvenile Nonfiction categories.

Bayesian Probability for Babies is a colorfully simple introduction to the basic principles of probability. If you took a bite out of a cookie and that bite has no candy in it, what is the probability that bite came from a candy cookie or a cookie with no candy? You and baby will find out the probability and discover it through different types of distribution. Yet another book of simple explanations of complex ideas for your future genius! Baby University: It only takes a small spark to ignite a child's mind.

Probability And Bayesian Modeling

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Author : Jim Albert
language : en
Publisher: CRC Press
Release Date : 2019-12-06

Probability And Bayesian Modeling written by Jim Albert and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-12-06 with Mathematics categories.

Probability and Bayesian Modeling is an introduction to probability and Bayesian thinking for undergraduate students with a calculus background. The first part of the book provides a broad view of probability including foundations, conditional probability, discrete and continuous distributions, and joint distributions. Statistical inference is presented completely from a Bayesian perspective. The text introduces inference and prediction for a single proportion and a single mean from Normal sampling. After fundamentals of Markov Chain Monte Carlo algorithms are introduced, Bayesian inference is described for hierarchical and regression models including logistic regression. The book presents several case studies motivated by some historical Bayesian studies and the authors’ research. This text reflects modern Bayesian statistical practice. Simulation is introduced in all the probability chapters and extensively used in the Bayesian material to simulate from the posterior and predictive distributions. One chapter describes the basic tenets of Metropolis and Gibbs sampling algorithms; however several chapters introduce the fundamentals of Bayesian inference for conjugate priors to deepen understanding. Strategies for constructing prior distributions are described in situations when one has substantial prior information and for cases where one has weak prior knowledge. One chapter introduces hierarchical Bayesian modeling as a practical way of combining data from different groups. There is an extensive discussion of Bayesian regression models including the construction of informative priors, inference about functions of the parameters of interest, prediction, and model selection. The text uses JAGS (Just Another Gibbs Sampler) as a general-purpose computational method for simulating from posterior distributions for a variety of Bayesian models. An R package ProbBayes is available containing all of the book datasets and special functions for illustrating concepts from the book. A complete solutions manual is available for instructors who adopt the book in the Additional Resources section.

Statistical Physics For Babies

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Author : Chris Ferrie
language : en
Publisher: Sourcebooks, Inc.
Release Date : 2018-01-02

Statistical Physics For Babies written by Chris Ferrie and has been published by Sourcebooks, Inc. this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-01-02 with Juvenile Nonfiction categories.

Help your future genius become the smartest baby in the room! Written by an expert, Statistical Physics for Babies is a colorfully simple introduction to the second law of thermodynamics. Babies (and grownups!) will learn all about entropy, probability, and more. With a tongue-in-cheek approach that adults will love, this installment of the Baby University board book series is the perfect way to introduce basic concepts to even the youngest scientists. After all, it's never too early to become a scientist! Baby University: It only takes a small spark to ignite a child's mind.

Neural Networks For Babies

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Author : Chris Ferrie
language : en
Publisher: Sourcebooks Jabberwocky
Release Date : 2019-03

Neural Networks For Babies written by Chris Ferrie and has been published by Sourcebooks Jabberwocky this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-03 with Juvenile Nonfiction categories.

Help your future genius become the smartest baby in the room by introducing them to neural networks with the next installment of the Baby University board book series! Set the children in your life on a lifelong path to learning with the next installment of the Baby University board book series. Full of scientific and mathematical information from an expert, this is the perfect book to teach complex concepts in a simple, engaging way. Neural Networks for Babies is a colorful, simple introduction to the study of how the creation of machines and computing systems is inspired by the biological neural networks in animal and human brains. It's never too early to become a scientist!

Bayes Rules

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Author : Alicia A. Johnson
language : en
Publisher: CRC Press
Release Date : 2022-03-03

Bayes Rules written by Alicia A. Johnson and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-03-03 with Mathematics categories.

Praise for Bayes Rules!: An Introduction to Applied Bayesian Modeling “A thoughtful and entertaining book, and a great way to get started with Bayesian analysis.” Andrew Gelman, Columbia University “The examples are modern, and even many frequentist intro books ignore important topics (like the great p-value debate) that the authors address. The focus on simulation for understanding is excellent.” Amy Herring, Duke University “I sincerely believe that a generation of students will cite this book as inspiration for their use of – and love for – Bayesian statistics. The narrative holds the reader’s attention and flows naturally – almost conversationally. Put simply, this is perhaps the most engaging introductory statistics textbook I have ever read. [It] is a natural choice for an introductory undergraduate course in applied Bayesian statistics." Yue Jiang, Duke University “This is by far the best book I’ve seen on how to (and how to teach students to) do Bayesian modeling and understand the underlying mathematics and computation. The authors build intuition and scaffold ideas expertly, using interesting real case studies, insightful graphics, and clear explanations. The scope of this book is vast – from basic building blocks to hierarchical modeling, but the authors’ thoughtful organization allows the reader to navigate this journey smoothly. And impressively, by the end of the book, one can run sophisticated Bayesian models and actually understand the whys, whats, and hows.” Paul Roback, St. Olaf College “The authors provide a compelling, integrated, accessible, and non-religious introduction to statistical modeling using a Bayesian approach. They outline a principled approach that features computational implementations and model assessment with ethical implications interwoven throughout. Students and instructors will find the conceptual and computational exercises to be fresh and engaging.” Nicholas Horton, Amherst College An engaging, sophisticated, and fun introduction to the field of Bayesian statistics, Bayes Rules!: An Introduction to Applied Bayesian Modeling brings the power of modern Bayesian thinking, modeling, and computing to a broad audience. In particular, the book is an ideal resource for advanced undergraduate statistics students and practitioners with comparable experience. Bayes Rules! empowers readers to weave Bayesian approaches into their everyday practice. Discussions and applications are data driven. A natural progression from fundamental to multivariable, hierarchical models emphasizes a practical and generalizable model building process. The evaluation of these Bayesian models reflects the fact that a data analysis does not exist in a vacuum. Features • Utilizes data-driven examples and exercises. • Emphasizes the iterative model building and evaluation process. • Surveys an interconnected range of multivariable regression and classification models. • Presents fundamental Markov chain Monte Carlo simulation. • Integrates R code, including RStan modeling tools and the bayesrules package. • Encourages readers to tap into their intuition and learn by doing. • Provides a friendly and inclusive introduction to technical Bayesian concepts. • Supports Bayesian applications with foundational Bayesian theory.

Bayesian Networks

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Author : Olivier Pourret
language : en
Publisher: John Wiley & Sons
Release Date : 2008-04-30

Bayesian Networks written by Olivier Pourret and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-04-30 with Mathematics categories.

Bayesian Networks, the result of the convergence of artificial intelligence with statistics, are growing in popularity. Their versatility and modelling power is now employed across a variety of fields for the purposes of analysis, simulation, prediction and diagnosis. This book provides a general introduction to Bayesian networks, defining and illustrating the basic concepts with pedagogical examples and twenty real-life case studies drawn from a range of fields including medicine, computing, natural sciences and engineering. Designed to help analysts, engineers, scientists and professionals taking part in complex decision processes to successfully implement Bayesian networks, this book equips readers with proven methods to generate, calibrate, evaluate and validate Bayesian networks. The book: Provides the tools to overcome common practical challenges such as the treatment of missing input data, interaction with experts and decision makers, determination of the optimal granularity and size of the model. Highlights the strengths of Bayesian networks whilst also presenting a discussion of their limitations. Compares Bayesian networks with other modelling techniques such as neural networks, fuzzy logic and fault trees. Describes, for ease of comparison, the main features of the major Bayesian network software packages: Netica, Hugin, Elvira and Discoverer, from the point of view of the user. Offers a historical perspective on the subject and analyses future directions for research. Written by leading experts with practical experience of applying Bayesian networks in finance, banking, medicine, robotics, civil engineering, geology, geography, genetics, forensic science, ecology, and industry, the book has much to offer both practitioners and researchers involved in statistical analysis or modelling in any of these fields.

General Relativity For Babies

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Author : Chris Ferrie
language : en
Publisher: Sourcebooks, Inc.
Release Date : 2017-05-02

General Relativity For Babies written by Chris Ferrie and has been published by Sourcebooks, Inc. this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-05-02 with Juvenile Nonfiction categories.

Fans of Chris Ferrie's ABCs of Science, Organic Chemistry for Babies, and Quantum Physics for Babies will love this introduction to Einstein's most famous theory! Help your future genius become the smartest baby in the room! It only takes a small spark to ignite a child's mind. Written by an expert, General Relativity for Babies is a colorfully simple introduction to Einstein's most famous theory. Babies (and grownups!) will learn all about black holes, gravitational waves, and more. With a tongue-in-cheek approach that adults will love, this installment of the Baby University board book series is the perfect way to introduce basic concepts to even the youngest scientists. After all, it's never too early to become a quantum physicist! If you're looking for books similar to Baby Loves Science by Ruth Spiro, quantum information for babies, or infant science books, look no further! General Relativity for Babies offers fun early learning for your little quantum physicist!

Bayesian Computation With R

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Author : Jim Albert
language : en
Publisher: Springer Science & Business Media
Release Date : 2007-07-07

Bayesian Computation With R written by Jim Albert and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-07-07 with Computers categories.

Bayesian Computation with R introduces Bayesian modeling by the use of computation using the R language. Early chapters present the basic tenets of Bayesian thinking by use of familiar one and two-parameter inferential problems. Bayesian computational methods such as Laplace's method, rejection sampling, and the SIR algorithm are illustrated in the context of a random effects model. The construction and implementation of Markov Chain Monte Carlo (MCMC) methods is introduced. These simulation-based algorithms are implemented for a variety of Bayesian applications such as normal and binary response regression, hierarchical modeling, order-restricted inference, and robust modeling.