Bayesian Inference
eBook - PDF

Bayesian Inference

Recent Trends

  1. 92 pages
  2. English
  3. PDF
  4. Available on iOS & Android
eBook - PDF

Bayesian Inference

Recent Trends

About this book

In an era where data is abundant and computational power is soaring, Bayesian Inference - Recent Trends emerges as an essential guide to understanding and applying Bayesian methods in various scientific and technological domains. This book uniquely blends theoretical rigor with practical insights, showcasing the latest advancements and applications of Bayesian inference.
•Discover the renaissance of Bayesian inference and its vital role in modern-day statistical analysis and prediction.
•Explore the depth of hidden Markov models and their power in inferring hidden states and transitions in stochastic systems.
•Dive into the complexity of nested sampling and its effectiveness in parameter estimation, particularly in signal processing.
•Examine the precision of naive Bayes algorithms in news classification, a critical task in the digital information age.
This book is an invaluable resource for anyone interested in the intersection of statistics, machine learning, and data science. It offers a unique perspective on Bayesian inference, revealing its potential to provide robust solutions in an increasingly data-driven world. Whether you are a seasoned researcher, a budding scientist, or a curious enthusiast, Bayesian Inference - Recent Trends is your gateway to understanding and leveraging the power of Bayesian methods in the ever-evolving landscape of data analysis.

Information

Publisher
IntechOpen
Year
2024
Print ISBN
9781837693566
eBook ISBN
9781837693559

Table of contents

  1. Bayesian Inference - Recent Trends
  2. Contents
  3. Preface
  4. Chapter1 Introductory Chapter: A Strong Come-Back of Bayesian Inference
  5. Chapter2 Indirect Observation of State and Transition Probabilities
  6. Chapter3 Nested Sampling: A Case Study in Parameter Estimation
  7. Chapter4 Bayesian Inference for Regularization and Model Complexity Control of Artificial Neural Networks in Classification Problems
  8. Chapter5 Performance Comparison between Naive Bayes and Machine Learning Algorithms for News Classification

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