Colocalization Estimation Using Graphical Modeling and Variational Bayesian Expectation Maximization: Towards a Parameter-Free Approach

2015 ◽  
pp. 3-16 ◽  
Author(s):  
Suyash P. Awate ◽  
Thyagarajan Radhakrishnan
Keyword(s):  
Expectation Maximization ◽  
Graphical Modeling ◽  
Variational Bayesian

scholarly journals Variational Bayesian Expectation Maximization for Radar Map Estimation

2016 ◽  
Vol 64 (6) ◽  
pp. 1391-1404 ◽  
Author(s):  
Malin Lundgren ◽  
Lennart Svensson ◽  
Lars Hammarstrand
Keyword(s):  
Expectation Maximization ◽  
Map Estimation ◽  
Variational Bayesian ◽  
Radar Map

Regularized Variational Bayesian Learning of Echo State Networks with Delay&Sum Readout

2012 ◽  
Vol 24 (4) ◽  
pp. 967-995 ◽  
Author(s):  
Dmitriy Shutin ◽  
Christoph Zechner ◽  
Sanjeev R. Kulkarni ◽  
H. Vincent Poor
Keyword(s):  
Expectation Maximization ◽  
Character Recognition ◽  
Bayesian Learning ◽  
Synthetic Data ◽  
Joint Estimation ◽  
Variational Bayesian ◽  
Training Scheme ◽  
Echo State Networks ◽  
Data Prediction ◽  
Superimposed Signals

In this work, a variational Bayesian framework for efficient training of echo state networks (ESNs) with automatic regularization and delay&sum (D&S) readout adaptation is proposed. The algorithm uses a classical batch learning of ESNs. By treating the network echo states as fixed basis functions parameterized with delay parameters, we propose a variational Bayesian ESN training scheme. The variational approach allows for a seamless combination of sparse Bayesian learning ideas and a variational Bayesian space-alternating generalized expectation-maximization (VB-SAGE) algorithm for estimating parameters of superimposed signals. While the former method realizes automatic regularization of ESNs, which also determines which echo states and input signals are relevant for “explaining” the desired signal, the latter method provides a basis for joint estimation of D&S readout parameters. The proposed training algorithm can naturally be extended to ESNs with fixed filter neurons. It also generalizes the recently proposed expectation-maximization-based D&S readout adaptation method. The proposed algorithm was tested on synthetic data prediction tasks as well as on dynamic handwritten character recognition.

scholarly journals Direct Policy Search Reinforcement Learning Based on Variational Bayesian Inference

2020 ◽  
Vol 24 (6) ◽  
pp. 711-718
Author(s):  
Nobuhiko Yamaguchi ◽  
Keyword(s):  
Reinforcement Learning ◽  
Bayesian Inference ◽  
Expectation Maximization ◽  
Weighted Regression ◽  
High Dimensional ◽  
Policy Search ◽  
Variational Bayesian ◽  
Learning Framework ◽  
Average Return ◽  
Variational Bayesian Inference

Direct policy search is a promising reinforcement learning framework particularly for controlling continuous, high-dimensional systems. Peters et al. proposed reward-weighted regression (RWR) as a direct policy search. The RWR algorithm estimates the policy parameter based on the expectation-maximization (EM) algorithm and is therefore prone to overfitting. In this study, we focus on variational Bayesian inference to avoid overfitting and propose direct policy search reinforcement learning based on variational Bayesian inference (VBRL). The performance of the proposed VBRL is assessed in several experiments involving a mountain car and a ball batting task. These experiments demonstrate that VBRL yields a higher average return and outperforms the RWR.

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