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Mastering Bayesian Analysis with Python
This book covers a wide range of topics and techniques in Bayesian analysis, providing readers with a comprehensive understanding of this powerful statistical method and its practical applications.
Starting with the fundamental concept of Bayes' theorem, the book dives into the mathematical foundation and definition, followed by its implementation in Python. You'll explore a variety of Bayesian modeling techniques, such as understanding prior and posterior distributions, likelihoods, and the application of conjugate priors.
The book also covers advanced topics like Markov Chain Monte Carlo (MCMC) algorithms, including Gibbs Sampling, Metropolis-Hastings Algorithm, Hamiltonian Monte Carlo (HMC), and No-U-Turn Sampler (NUTS). With detailed explanations and step-by-step Python implementations, you'll gain valuable hands-on experience.
Additionally, the book covers approximation methods like Approximate Bayesian Computation (ABC), Variational Inference, Expectation-Maximization (EM) Algorithm, Sequential Monte Carlo Methods (SMC), Particle Filters, and Kalman Filters. You'll also explore Bayesian modeling for linear regression, logistic regression, Gaussian processes, Dirichlet processes, latent Dirichlet allocation (LDA), hidden Markov models (HMM), Bayesian theorem networks, and hierarchical models.
Throughout the book, you'll find practical code examples in Python, making it easy to apply the concepts to real-world scenarios. Whether you're a beginner or an experienced data scientist, this book will equip you with the necessary skills to confidently analyze and model data using Bayesian methods.
Starting with the fundamental concept of Bayes' theorem, the book dives into the mathematical foundation and definition, followed by its implementation in Python. You'll explore a variety of Bayesian modeling techniques, such as understanding prior and posterior distributions, likelihoods, and the application of conjugate priors.
The book also covers advanced topics like Markov Chain Monte Carlo (MCMC) algorithms, including Gibbs Sampling, Metropolis-Hastings Algorithm, Hamiltonian Monte Carlo (HMC), and No-U-Turn Sampler (NUTS). With detailed explanations and step-by-step Python implementations, you'll gain valuable hands-on experience.
Additionally, the book covers approximation methods like Approximate Bayesian Computation (ABC), Variational Inference, Expectation-Maximization (EM) Algorithm, Sequential Monte Carlo Methods (SMC), Particle Filters, and Kalman Filters. You'll also explore Bayesian modeling for linear regression, logistic regression, Gaussian processes, Dirichlet processes, latent Dirichlet allocation (LDA), hidden Markov models (HMM), Bayesian theorem networks, and hierarchical models.
Throughout the book, you'll find practical code examples in Python, making it easy to apply the concepts to real-world scenarios. Whether you're a beginner or an experienced data scientist, this book will equip you with the necessary skills to confidently analyze and model data using Bayesian methods.
Kindle Edition
Published July 8, 2024
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