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Author Lawrence, Neil D. ♦ House, St. George ♦ Azzouzi, Mehdi
Source CiteSeerX
Content type Text
File Format PDF
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Subject Keyword Bayesian Neural Network Posterior ♦ Bayesian Neural Network ♦ Variational Inference ♦ Evidence Procedure ♦ Monte Carlo Sampling ♦ Gaussian Distribution ♦ Exact Inference ♦ Posterior Distribution ♦ Result Approximate Approach
Abstract Exact inference in Bayesian neural networks is non analytic to compute and as a result approximate approaches such as the evidence procedure, Monte Carlo sampling and variational inference have been proposed. In this paper we explore the structure of the posterior distributions in a Bayesian neural network through these approximations and a new variational approximating distribution based on mixtures of Gaussian distributions.
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research
Education Level UG and PG ♦ Career/Technical Study
Learning Resource Type Article