Inference And Learning From Data

Statistical Methods for Dynamic Treatment Regimes shares state of the art of statistical methods developed to address questions of estimation and inference for dynamic treatment regimes, a branch of personalized medicine.

This book describes the important ideas in a variety of fields such as medicine, biology, finance, and marketing in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of colour graphics.

This workshop series was initiated by Myron Tribus and Edwin T. Jaynes, and organised for the first time by C.Ray Smith and Walter T. Grandy in 1981 at the University in Wyoming. Since then, tremendous progress has been made and the number of publications based on Bayesian methods is impressive

This fully updated new edition of a uniquely accessible textbook/reference provides a general introduction to probabilistic graphical models (PGMs) from an engineering perspective. It features new material on partially observable Markov decision processes, graphical models, and deep learning, as well as an even greater number of exercises.

Build, Manage, and Deploy Machine Learning (AI) Projects with Python and MLOps