A framework for human pose estimation by integrating data-driven Markov chain Monte Carlo with multi-objective evolutionary algorithm

2006 ◽  
Author(s):  
Shih-Shinh Huang ◽  
Li-Chen Fu ◽  
Pei-Yung Hsiao
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Evolutionary Algorithm ◽  
Pose Estimation ◽  
Data Driven ◽  
Human Pose Estimation ◽  
Multi Objective ◽  
Human Pose

Partitioning gene expression data by data-driven Markov chain Monte Carlo

2015 ◽  
Vol 43 (6) ◽  
pp. 1155-1173 ◽  
Author(s):  
E.F. Saraiva ◽  
A.K. Suzuki ◽  
F. Louzada ◽  
L.A. Milan
Keyword(s):  
Gene Expression ◽  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Gene Expression Data ◽  
Data Driven ◽  
Expression Data

Issues in Bayesian Analysis of Neural Network Models

1998 ◽  
Vol 10 (3) ◽  
pp. 749-770 ◽  
Author(s):  
Peter Müller ◽  
David Rios Insua
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Bayesian Analysis ◽  
Network Models ◽  
Feedforward Neural Networks ◽  
Data Driven ◽  
Neural Network Models

Stemming from work by Buntine and Weigend (1991) and MacKay (1992), there is a growing interest in Bayesian analysis of neural network models. Although conceptually simple, this problem is computationally involved. We suggest a very efficient Markov chain Monte Carlo scheme for inference and prediction with fixed-architecture feedforward neural networks. The scheme is then extended to the variable architecture case, providing a data-driven procedure to identify sensible architectures.

Data Driven Markov Chain Monte Carlo

2020 ◽  
pp. 211-280
Author(s):  
Adrian Barbu ◽  
Song-Chun Zhu
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Data Driven
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