✦ LIBER ✦

Maximum entropy inference for mixed continuous-discrete variables

✍ Scribed by Hermann Singer

Publisher: John Wiley and Sons
Year: 2010
Tongue: English
Weight: 266 KB
Volume: 25
Category: Article
ISSN: 0884-8173
DOI: 10.1002/int.20411

No coin nor oath required. For personal study only.

✦ Synopsis

We represent knowledge by probability distributions of mixed continuous and discrete variables. From the joint distribution of all items, one can compute arbitrary conditional distributions, which may be used for prediction. However, in many cases only some marginal distributions, inverse probabilities, or moments are known. Under these conditions, a principle is needed to determine the full joint distribution of all variables. The principle of maximum entropy (Jaynes,

📜 SIMILAR VOLUMES

Entropy and MDL discretization of contin

Entropy and MDL discretization of continuous variables for Bayesian belief networks

✍ Ellis J. Clarke; Bruce A. Barton 📂 Article 📅 2000 🏛 John Wiley and Sons 🌐 English ⚖ 313 KB 👁 2 views

An efficient algorithm for partitioning the range of a continuous variable to a discrete Ž . number of intervals, for use in the construction of Bayesian belief networks BBNs , is presented here. The partitioning minimizes the information loss, relative to the number of intervals used to represent t

Covariance Adjustments in Discrimination

Covariance Adjustments in Discrimination of Mixed Discrete and Continuous Variables

✍ Chi-Ying Leung 📂 Article 📅 1999 🏛 Elsevier Science 🌐 English ⚖ 126 KB

Sufficient conditions are given to ensure a better performance of the plug-in version of the covariates adjusted location linear discriminant function in an asymptotic comparison of the overall expected error rate. Our findings generalize several earlier results on discriminant function with covaria

Introduction to Bayesian Statistics (Bol

Introduction to Bayesian Statistics (Bolstad/Bayesian Statistics) || Bayesian Inference for Discrete Random Variables

✍ Bolstad, William M. 📂 Article 📅 2005 🏛 John Wiley & Sons, Inc. 🌐 English ⚖ 146 KB 👁 1 views

Scientific Data Gathering -- Displaying And Summarizing Data -- Logic, Probability, And Uncertainty -- Discrete Random Variables -- Bayesian Inference For Discrete Random Variables -- Continuous Random Variables -- Bayesian Inference For Binomial Proportion -- Comparing Bayesian And Frequentist Infe

Nearest Neighbor Classification Rule for

Nearest Neighbor Classification Rule for Mixtures of Discrete and Continuous Random Variables

✍ T. J. Wojciechowski 📂 Article 📅 1987 🏛 John Wiley and Sons 🌐 English ⚖ 297 KB 👁 2 views

In this paper very simple nonparametric c l d i c a t i o n rule for mixtures of discrete and oonhuons random variables is described. It ie based on the method of neatest neighbor proposed by COVEB and HABT (1967). The bounds on the limit of the near& neighbor ruleriske are given. Both lower and upp

The Empirical Bayes Classification Rules

The Empirical Bayes Classification Rules for Mixtures of Discrete and Continuous Variables

✍ T. Wojciechowski 📂 Article 📅 1985 🏛 John Wiley and Sons 🌐 English ⚖ 603 KB

Let us consider a general population R. Each object belonging to the population R is characterized by a pair of correlated random vectors (& I). Both X and \_Y may be mixtures of discrete and continuous random variables. It will be assumed that our population R consists of k groups nl, ..., 3zk, whi

Discrete maximum principles for FE solut

Discrete maximum principles for FE solutions of nonstationary diffusion-reaction problems with mixed boundary conditions

✍ István Faragó; Róbert Horváth; Sergey Korotov 📂 Article 📅 2009 🏛 John Wiley and Sons 🌐 English ⚖ 270 KB

In this article, we derive and discuss sufficient conditions for providing validity of the discrete maximum principle for nonstationary diffusion-reaction problems with mixed boundary conditions, solved by means of simplicial finite elements and the θ time discretization method. The theoretical anal