Bayesian Field Theory
by J. C. Lemm
Publisher: arXiv.org 2000
Number of pages: 200
Description:
Bayesian field theory denotes a nonparametric Bayesian approach for learning functions from observational data. Based on the principles of Bayesian statistics, a particular Bayesian field theory is defined by combining two models: a likelihood model, providing a probabilistic description of the measurement process, and a prior model, providing the information necessary to generalize from training to non-training data.
Download or read it online for free here:
Download link
(1.7MB, PDF)
Similar books
Convergence of Stochastic Processesby D. Pollard - Springer
Selected parts of empirical process theory, with applications to mathematical statistics. The book describes the combinatorial ideas needed to prove maximal inequalities for empirical processes indexed by classes of sets or classes of functions.
(16310 views)
Lectures on Noise Sensitivity and Percolationby Christophe Garban, Jeffrey E. Steif - arXiv
The goal of this set of lectures is to combine two seemingly unrelated topics: (1) The study of Boolean functions, a field particularly active in computer science; (2) Some models in statistical physics, mostly percolation.
(12526 views)
Probability and Statistics Cookbookby Matthias Vallentin
The cookbook contains a succinct representation of various topics in probability theory and statistics. It provides a comprehensive reference reduced to the mathematical essence, rather than aiming for elaborate explanations.
(20085 views)
Seeing Theory: A visual introduction to probability and statisticsby T. Devlin, J. Guo, D. Kunin, D. Xiang - Brown University
The intent of the website and these notes is to provide an intuitive supplement to an introductory level probability and statistics course. The level is also aimed at students who are returning to the subject and would like a concise refresher ...
(9145 views)