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!

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.

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.

Help your future genius become the smartest baby in the room! Written by an expert, Optical Physics for Babies is a colorfully simple introduction to the principles of linear optics. Babies (and grownups!) will learn the difference between reflection and refraction and why both are necessary to create wonderful things like rainbows. 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 physicist! Baby University: It only takes a small spark to ignite a child's mind.

Fans of Chris Ferrie's Rocket Science for Babies, Quantum Physics for Babies, and 8 Little Planets will love this introduction to organic chemistry for babies and toddlers! It only takes a small spark to ignite a child's mind. Written by an expert, Organic Chemistry for Babies is a colorfully simple introduction to the structure of organic, carbon-containing compounds and materials. Gift your special little one the opportunity to learn with this perfect science baby gift and help them be one step ahead of pre-med students! With a tongue-in-cheek approach that adults will love, this installment of the Baby University baby board book series is the perfect way to introduce STEM concepts for babies and toddlers. After all, it's never too early to become an organic chemist! If you're looking for the perfect STEAM book for teachers, science toys for babies, or chemistry toys for kids, look no further! Organic Chemistry for Babies offers fun early learning for your little scientist!

Fans of Chris Ferrie's ABCs of Economics, ABCs of Space, and Organic Chemistry for Babies will love this introduction to neural networks 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. Neural Networks for Babies by Chris Ferrie is a colorfully simple introduction to the study of how machines and computing systems are created in a way that was inspired by the biological neural networks in animal and human brains. With scientific and mathematical information from an expert, this installment of the Baby University board book series is the perfect book for enlightening the next generation of geniuses. After all, it's never too early to become a scientist! If you're looking for programming for babies, coding for babies, or more Baby University board books to surprise your little one, look no further! Neural Networks for Babies offers fun early learning for your little scientist!

The first edition of Bayesian Methods: A Social and Behavioral Sciences Approach helped pave the way for Bayesian approaches to become more prominent in social science methodology. While the focus remains on practical modeling and basic theory as well as on intuitive explanations and derivations without skipping steps, this second edition incorporates the latest methodology and recent changes in software offerings. New to the Second Edition Two chapters on Markov chain Monte Carlo (MCMC) that cover ergodicity, convergence, mixing, simulated annealing, reversible jump MCMC, and coupling Expanded coverage of Bayesian linear and hierarchical models More technical and philosophical details on prior distributions A dedicated R package (BaM) with data and code for the examples as well as a set of functions for practical purposes such as calculating highest posterior density (HPD) intervals Requiring only a basic working knowledge of linear algebra and calculus, this text is one of the few to offer a graduate-level introduction to Bayesian statistics for social scientists. It first introduces Bayesian statistics and inference, before moving on to assess model quality and fit. Subsequent chapters examine hierarchical models within a Bayesian context and explore MCMC techniques and other numerical methods. Concentrating on practical computing issues, the author includes specific details for Bayesian model building and testing and uses the R and BUGS software for examples and exercises.