**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

**Random Matrix Models and Their Applications**

by

**Pavel Bleher, Alexander Its**-

**Cambridge University Press**

The book covers broad areas such as topologic and combinatorial aspects of random matrix theory; scaling limits, universalities and phase transitions in matrix models; universalities for random polynomials; and applications to integrable systems.

(

**11100**views)

**Inverse Problem Theory and Methods for Model Parameter Estimation**

by

**Albert Tarantola**-

**SIAM**

The first part deals with discrete inverse problems with a finite number of parameters, while the second part deals with general inverse problems. The book for scientists and applied mathematicians facing the interpretation of experimental data.

(

**11154**views)

**Seeing Theory: A visual introduction to probability and statistics**

by

**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 ...

(

**1122**views)

**A defense of Columbo: A multilevel introduction to probabilistic reasoning**

by

**G. D'Agostini**-

**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.

(

**10881**views)