Bayesian reasoning and Gaussian processes for machine learning applications / edited by Hemachandran K., [and 4 others].

Includes bibliographical references and index.

Introduction to naive Bayes and a review on its subtypes with applications / Eguturi Manjith Kumar Reddy, Akash Gurrala, Vasireddy Bindu Hasitha, Korupalli V. Rajesh Kumar -- A review on different regression analysis in supervised learning / K. Sudhaman, Mahesh Akuthota and Sandip Kumar Chaurasiya -- Methods to predict the performance analysis of various machine learning algorithms / M. Saritha, M. Lavanya and M. Narendra Reddy -- A viewpoint on belief networks and their applications / G.S. Sivakumar, P. Suneetha, V. Sailaja and Pokala Pranay Kumar -- Reinforcement learning using Bayesian algorithms with applications / H. Raghupathi, G. Ravi and Rajan Maduri -- Alerting system for gas leakage in pipeline / Nilesh Deotale, Pragya Chandra, Prathamesh Dherange, Pratiksha Repaswal, Saibaba V. More -- New non-parametric models for biological networks / Deniz Seçilmiş, Melih Ağraz, Vilda Purutçuoğlu -- Generating various types of graphical models via MARS / Ezgi Ayyıldız and Vilda Purutçuoğlu -- Financial applications of Gaussian processes and Bayesian optimization / Syed Hasan Jafar -- Bayesian network inference on diabetes risk prediction data / Mustafa Özgür Cingiz.

"The book Bayesian Reasoning and Gaussian Processes for Machine Learning Applications talks about Bayesian Reasoning and Gaussian Processes in machine learning applications. Bayesian methods are applied in many areas such as game development, decision making and drug discovery. It is very effective for machine learning algorithms for handling missing data and for extracting information from small datasets. This book introduces a statistical background which is needed to understand continuous distributions and it gives an understanding on how learning can be viewed from a probabilistic framework. The chapters of the book progress into machine learning topics such as Belief Network, Bayesian Reinforcement Learning etc., which is followed by Gaussian Process Introduction, Classification, Regression, Covariance and Performance Analysis of GP with other models. This book is aimed primarily at graduates, researchers and professionals in the field of data science and machine learning"-- Provided by publisher.