neural coding & computation group
Bayesian Entropy estimator for binary vector observations - matlab code |
description: Computes the Bayes' least squares estimate (i.e., posterior mean) of the entropy of a discrete distribution over binary vectors under a Centered Dirichlet Mixture (CDM) prior. The CDM prior is a mixture of Dirichlet distributions with base measure given by either a parametric Bernoulli prior (DBer) or population synchrony (DSyn) prior. The base measure serves to regularize the estimate of the discrete probability distribution: (1) the Bernoulli prior specifies the (independent) probability of a spike p for each neuron, which is useful when spikes are rare. (2) The population synchrony prior specifies a distribution over the number of total spikes in a word (P(0 spikes), P(1 spike), P(2 spikes),...). download: master.zip (more info: README.html) browse: github project page relevant publication: |