Probabilistic Inference by means of Cluster Variation Method and Linear Response Theory

Kazuyuki TANAKA

Probabilistic Inference by means of Cluster Variation Method and Linear Response Theory

Kazuyuki TANAKA

Full Text Views

0

Share
Cite this

Summary :

Probabilistic inference by means of a massive probabilistic model usually has exponential-order computational complexity. For such massive probabilistic model, loopy belief propagation was proposed as a scheme to obtain the approximate inference. It is known that the generalized loopy belief propagation is constructed by using a cluster variation method. However, it is difficult to calculate the correlation in every pair of nodes which are not connected directly to each other by means of the generalized loopy belief propagation. In the present paper, we propose a general scheme for calculating an approximate correlation in every pair of nodes in a probabilistic model for probabilistic inference. The general scheme is formulated by combining a cluster variation method with a linear response theory.

Publication: IEICE TRANSACTIONS on Information Vol.E86-D No.7 pp.1228-1242

Publication Date: 2003/07/01

Publicized

Online ISSN

DOI

Type of Manuscript: PAPER

Category: Algorithms

Cite this

Copy

Kazuyuki TANAKA, "Probabilistic Inference by means of Cluster Variation Method and Linear Response Theory" in IEICE TRANSACTIONS on Information, vol. E86-D, no. 7, pp. 1228-1242, July 2003, doi: .
Abstract: Probabilistic inference by means of a massive probabilistic model usually has exponential-order computational complexity. For such massive probabilistic model, loopy belief propagation was proposed as a scheme to obtain the approximate inference. It is known that the generalized loopy belief propagation is constructed by using a cluster variation method. However, it is difficult to calculate the correlation in every pair of nodes which are not connected directly to each other by means of the generalized loopy belief propagation. In the present paper, we propose a general scheme for calculating an approximate correlation in every pair of nodes in a probabilistic model for probabilistic inference. The general scheme is formulated by combining a cluster variation method with a linear response theory.
URL: https://globals.ieice.org/en_transactions/information/10.1587/e86-d_7_1228/_p

Copy

@ARTICLE{e86-d_7_1228,
author={Kazuyuki TANAKA, },
journal={IEICE TRANSACTIONS on Information},
title={Probabilistic Inference by means of Cluster Variation Method and Linear Response Theory},
year={2003},
volume={E86-D},
number={7},
pages={1228-1242},
abstract={Probabilistic inference by means of a massive probabilistic model usually has exponential-order computational complexity. For such massive probabilistic model, loopy belief propagation was proposed as a scheme to obtain the approximate inference. It is known that the generalized loopy belief propagation is constructed by using a cluster variation method. However, it is difficult to calculate the correlation in every pair of nodes which are not connected directly to each other by means of the generalized loopy belief propagation. In the present paper, we propose a general scheme for calculating an approximate correlation in every pair of nodes in a probabilistic model for probabilistic inference. The general scheme is formulated by combining a cluster variation method with a linear response theory.},
keywords={},
doi={},
ISSN={},
month={July},}

Copy

TY - JOUR
TI - Probabilistic Inference by means of Cluster Variation Method and Linear Response Theory
T2 - IEICE TRANSACTIONS on Information
SP - 1228
EP - 1242
AU - Kazuyuki TANAKA
PY - 2003
DO -
JO - IEICE TRANSACTIONS on Information
SN -
VL - E86-D
IS - 7
JA - IEICE TRANSACTIONS on Information
Y1 - July 2003
AB - Probabilistic inference by means of a massive probabilistic model usually has exponential-order computational complexity. For such massive probabilistic model, loopy belief propagation was proposed as a scheme to obtain the approximate inference. It is known that the generalized loopy belief propagation is constructed by using a cluster variation method. However, it is difficult to calculate the correlation in every pair of nodes which are not connected directly to each other by means of the generalized loopy belief propagation. In the present paper, we propose a general scheme for calculating an approximate correlation in every pair of nodes in a probabilistic model for probabilistic inference. The general scheme is formulated by combining a cluster variation method with a linear response theory.
ER -