"Bayesian Inference: Recent Trends" ed. by İhsan Bucak

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. The book is your gateway to understanding and leveraging the power of Bayesian methods in the ever-evolving landscape of data analysis.

In an era where data is abundant and computational power is soaring, this book 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.

Contents
1. A Strong Come-Back of Bayesian Inference
2. Indirect Observation of State and Transition Probabilities
3. Nested Sampling: A Case Study in Parameter Estimation
4. Bayesian Inference for Regularization and Model Complexity Control of Artificial Neural Networks in Classification Problems
5. Performance Comparison between Naive Bayes and Machine Learning Algorithms for News Classification


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