3 research outputs found
BayesPy: Variational Bayesian Inference in Python
BayesPy is an open-source Python software package for performing variational
Bayesian inference. It is based on the variational message passing framework
and supports conjugate exponential family models. By removing the tedious task
of implementing the variational Bayesian update equations, the user can
construct models faster and in a less error-prone way. Simple syntax, flexible
model construction and efficient inference make BayesPy suitable for both
average and expert Bayesian users. It also supports some advanced methods such
as stochastic and collapsed variational inference.Comment: Submitted to Journal of Machine Learning Research - Machine Learning
Open Source Softwar
Accelerating cyclic update algorithms for parameter estimation by pattern searches
Abstract. A popular strategy for dealing with large parameter estimation problems is to split the problem into manageable subproblems and solve them cyclically one by one until convergence. A well-known drawback of this strategy is slow convergence in low noise conditions. We propose using so-called pattern searches which consist of an exploratory phase followed by a line search. During the exploratory phase, a search direction is determined by combining the individual updates of all subproblems. The approach can be used to speed up several well-known learning methods such as variational Bayesian learning (ensemble learning) and expectation-maximization algorithm with modest algorithmic modifications. Experimental results show that the proposed method is able to reduce the required convergence time by 60β85 % in realistic variational Bayesian learning problems
Accelerating Cyclic Update Algorithms for Parameter Estimation by Pattern Searches
A popular strategy for dealing with large parameter estimation problems is to split the problem into manageable subproblems and solve them cyclically one by one until convergence. A well-known drawback of this strategy is slow convergence in low noise conditions. We propose using so-called pattern searches which consist of an exploratory phase followed by a line search. During the exploratory phase, a search direction is determined by combining the individual updates of all subproblems. The approach can be used to speed up several well-known learning methods such as variational Bayesian learning (ensemble learning) and expectation-maximization algorithm with modest algorithmic modifications. Experimental results show that the proposed method is able to reduce the required convergence time by 60--85 % in realistic variational Bayesian learning problems