Laser-based detection and tracking moving objects using data-driven Markov chain Monte Carlo

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.

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

Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006. ◽

10.1109/robot.2006.1642275 ◽

2006 ◽

Cited By ~ 1

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

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Data Driven Markov Chain Monte Carlo

Monte Carlo Methods ◽

10.1007/978-981-13-2971-5_8 ◽

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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Efficient posterior sampling for high-dimensional imbalanced logistic regression

Biometrika ◽

10.1093/biomet/asaa035 ◽

2020 ◽

Vol 107 (4) ◽

pp. 1005-1012 ◽

Cited By ~ 1

Author(s):

Deborshee Sen ◽

Matthias Sachs ◽

Jianfeng Lu ◽

David B Dunson

Keyword(s):

Monte Carlo ◽

Markov Chain ◽

Markov Chain Monte Carlo ◽

Imbalanced Data ◽

Simulated Data ◽

Computational Time ◽

High Dimensional ◽

Cancer Data ◽

Monte Carlo Algorithms ◽

Using Data

Summary Classification with high-dimensional data is of widespread interest and often involves dealing with imbalanced data. Bayesian classification approaches are hampered by the fact that current Markov chain Monte Carlo algorithms for posterior computation become inefficient as the number $p$ of predictors or the number $n$ of subjects to classify gets large, because of the increasing computational time per step and worsening mixing rates. One strategy is to employ a gradient-based sampler to improve mixing while using data subsamples to reduce the per-step computational complexity. However, the usual subsampling breaks down when applied to imbalanced data. Instead, we generalize piecewise-deterministic Markov chain Monte Carlo algorithms to include importance-weighted and mini-batch subsampling. These maintain the correct stationary distribution with arbitrarily small subsamples and substantially outperform current competitors. We provide theoretical support for the proposed approach and demonstrate its performance gains in simulated data examples and an application to cancer data.

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