Titlebook: Learning in Graphical Models; Michael I. Jordan Book 1998 Springer Science+Business Media Dordrecht 1998 Bayesian network.Latent variable

ACME

Latent Variable Modelsining a joint distribution over visible and latent variables, the corresponding distribution of the observed variables is then obtained by marginalization. This allows relatively complex distributions to be expressed in terms of more tractable joint distributions over the expanded variable space. On

Amorous

Stochastic Algorithms for Exploratory Data Analysis: Data Clustering and Data Visualizationllow the data analyst to detect structure in vectorial or relational data. Conceptually, the clustering and visualization procedures are formulated as combinatorial or continuous optimization problems which are solved by stochastic optimization.

羽毛長成

Learning Bayesian Networks with Local Structureach explicitly represents and learns the . in the . (CPDs) that quantify these networks. This increases the space of possible models, enabling the representation of CPDs with a variable number of parameters. The resulting learning procedure induces models that better emulate the interactions present

Affirm

FEAS

Bucket Elimination: A Unifying Framework for Probabilistic Inference inference literature and clarifies the relationship of such algorithms to nonserial dynamic programming algorithms. A general method for combining conditioning and bucket elimination is also presented. For all the algorithms, bounds on complexity are given as a function of the problem’s structure.

好開玩笑

Improving the Mean Field Approximation Via the Use of Mixture Distributionssterior is multi-modal, only one of the modes can be captured. To improve the mean field approximation in such cases, we employ mixture models as posterior approximations, where each mixture component is a factorized distribution. We describe efficient methods for optimizing the Parameters in these models.

Slit-Lamp

Introduction to Monte Carlo Methodsnte Carlo methods is presented. The chapter concludes with a discussion of advanced methods, including methods for reducing random walk behaviour..For details of Monte Carlo methods, theorems and proofs and a full list of references, the reader is directed to Neal (1993), Gilks, Richardson and Spiegelhalter (1996), and Tanner (1996).

商議

parasite

Learning Bayesian Networks with Local Structuretances, than those of the standard procedure, which ignores the local structure of the CPDs. Our results also show that networks learned with local structures tend to be more complex (in terms of arcs), yet require fewer parameters.

典型

An Introduction to Variational Methods for Graphical Modelsound for local probabilities, and discussing methods for extending these bounds to bounds on global probabilities of interest. Finally we return to the examples and demonstrate how variational algorithms can be formulated in each case.