Logo

Bayesian Field Theory by J. C. Lemm

Large book cover: Bayesian Field Theory

Bayesian Field Theory
by

Publisher: arXiv.org
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.

Home page url

Download or read it online for free here:
Download link
(1.7MB, PDF)

Similar books

Book cover: Bayesian Spectrum Analysis and Parameter EstimationBayesian Spectrum Analysis and Parameter Estimation
by - Springer
This work is a research document on the application of probability theory to the parameter estimation problem. The people who will be interested in this material are physicists, economists, and engineers who have to deal with data on a daily basis.
(11890 views)
Book cover: Probability, Statistics and Stochastic ProcessesProbability, Statistics and Stochastic Processes
by
Contents: Probability (Probability Calculus, Random Variables, Discrete and Continuous Distributions); Statistics (Handling of Data, Sampling, Estimation, Hypothesis Testing); Stochastic Processes (Markov Processes, Continuous-Time Processes).
(6778 views)
Book cover: A defense of Columbo: A multilevel introduction to probabilistic reasoningA defense of Columbo: A multilevel introduction to probabilistic reasoning
by - arXiv
Triggered by a recent interesting article on the too frequent incorrect use of probabilistic evidence in courts, the author introduces the basic concepts of probabilistic inference with a toy model, and discusses several important issues.
(10985 views)
Book cover: Introduction Probaility and StatisticsIntroduction Probaility and Statistics
by - University of Southern Maine
Topics: Data Analysis; Probability; Random Variables and Discrete Distributions; Continuous Probability Distributions; Sampling Distributions; Point and Interval Estimation; Large Sample Estimation; Large-Sample Tests of Hypothesis; etc.
(21554 views)