bayesian learning 
The approach can be used to speed up several wellknown learning methods such as variational Bayesian learning (ensemble learning) and expectationmaximization algorithm with modest algorithmic modifications.


Experimental results show that the proposed method is able to reduce the required convergence time by 6085% in realistic variational Bayesian learning problems.


Bayesian learning leads to correlated equilibria in normal form games


We formulate an infinitehorizon Bayesian learning model in which the planner faces a cost from switching actions that does not approach zero as the size of the change vanishes.


Bayesian learning and convergence to Nash equilibria without common priors


This finding suggests that players did not use sophisticated Bayesian learning to reach the mixedstrategy Nash equilibrium.


Bayesian learning in repeated games of incomplete information


stage game payoff functions are common knowledge), it is impossible to construct a Bayesian learning theory in which player beliefs are simultaneously weakly cautious, symmetric, and consistent.


Mineral Potential Mapping Using Bayesian Learning for Multilayer Perceptrons


In contrast to gradient descent methods, Bayesian learning techniques do not find a single weight vector; rather, they infer the posterior distribution of the weights given the data.


This paper reports results of applying Bayesian learning techniques to the production of maps representing gold mineralization potential over the Castlemaine region of Victoria, Australia.


Realtime braincomputer interfacing: A preliminary study using Bayesian learning


The neural networks are trained with a Bayesian learning scheme.


In this paper reliability is achieved by Walley's imprecise Dirichlet model, which generalizes Bayesian learning with Dirichlet priors.


Quantifying the Community: Using Bayesian Learning Networks to find Structure and Conduct Inference in Invasions Biology


Bayesian Learning Networks (BLNs) combine graphical models with multivariate Bayesian statistics to provide an analytical tool for the quantification of communities.


Bayesian learning is based on measuring the predictive ability of a model in terms of the corresponding marginal data distribution, which equals the expectation of the likelihood with respect to a prior distribution for model parameters.


Here we introduce a novel Bayesian learning method based on the predictive entropy of a probability model, that can combine both subjective and objective probabilistic assessment of uncertain quantities in putative models.


In this paper we review SciTegic's methodology for molecular fingerprints, molecular similarity, molecular clustering, maximal common subgraph search and Bayesian learning.


Bayesian learning models with revision of evidence

