Integrating bottom-up/top-down for object recognition by data driven Markov chain Monte Carlo

2002 ◽  
Author(s):  
Song-Chun Zhu ◽  
Rong Zhang ◽  
Zhuowen Tu
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Object Recognition ◽  
Markov Chain Monte Carlo ◽  
Data Driven ◽  
Top Down ◽  
Bottom Up

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

scholarly journals Bayesian Connective Field Modeling: a Markov Chain Monte Carlo approach

2020 ◽  
Author(s):  
Azzurra Invernizzi ◽  
Koen V. Haak ◽  
Joana C. Carvalho ◽  
Remco J. Renken ◽  
Frans W. Cornelissen
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Visual System ◽  
Effect Size ◽  
Data Driven ◽  
Cortical Circuitry ◽  
Early Visual System ◽  
The Brain ◽  
Mcmc Approach

AbstractThe majority of neurons in the human brain process signals from neurons elsewhere in the brain. Connective Field (CF) modeling is a biologically-grounded method to describe this essential aspect of the brain’s circuitry. It allows characterizing the response of a population of neurons in terms of the activity in another part of the brain. CF modeling translates the concept of the receptive field (RF) into the domain of connectivity by assessing the spatial dependency between signals in distinct cortical visual field areas. Standard CF model estimation has some intrinsic limitations in that it cannot estimate the uncertainty associated with each of its parameters. Obtaining the uncertainty will allow identification of model biases, e.g. related to an over- or under-fitting or a co-dependence of parameters, thereby improving the CF prediction. To enable this, here we present a Bayesian framework for the CF model. Using a Markov Chain Monte Carlo (MCMC) approach, we estimate the underlying posterior distribution of the CF parameters and consequently, quantify the uncertainty associated with each estimate. We applied the method and its new Bayesian features to characterize the cortical circuitry of the early human visual cortex of 12 healthy participants that were assessed using 3T fMRI. In addition, we show how the MCMC approach enables the use of effect size (beta) as a data-driven parameter to retain relevant voxels for further analysis. Finally, we demonstrate how our new method can be used to compare different CF models. Our results show that single Gaussian models are favoured over differences of Gaussians (i.e. center-surround) models, suggesting that the cortico-cortical connections of the early visual system do not possess center-surround organisation. We conclude that our new Bayesian CF framework provides a comprehensive tool to improve our fundamental understanding of the human cortical circuitry in health and disease.Highlights□ We present and validate a Bayesian CF framework based on a MCMC approach.□ The MCMC CF approach quantifies the model uncertainty associated with each CF parameter.□ We show how to use effect size beta as a data-driven threshold to retain relevant voxels.□ The cortical connective fields of the human early visual system are best described by a single, circular symmetric, Gaussian.

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