Importance sampling is related to rejection sampling, which I looked at in the last post. Here is a short demo. %% true probability distribution true_func = @(x) betapdf(x,1+1,1+10); %% Do importance sampling N = 10^6; % uniform proposal distribution x_samples = rand(N,1); proposal = 1/N; % evaluate for each sample target = true_func(x_samples); % calculate importance …

# Tag archives: Bayes

## Hierarchical Bayesian estimation and hypothesis testing for delay discounting tasks

I am happy to announce my 3rd paper of the year, accepted for publication in Behavior Research Methods. Following my initial foray into writing review papers (2 earlier this year), this is my first methods paper, and also my first contribution to higher-level decision making.

## Bayesian accounts of covert selective attention: a tutorial review

I am very happy to announce my new tutorial review paper. Vincent, B. T. (2015) Bayesian accounts of covert selective attention: a tutorial review, Attention, Perception, & Psychophysics, 77(4), 1013-1032. If you do not have an institutional subscription to Attention, Perception, & Psychophysics, Springer allow me to self-archive my author-accepted manuscript (legal). Get the preprints here: [manuscript pdf], [supplementary pdf]. The final publication …

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## Plotting posterior predictive distributions

I’ve just released a small bit of Matlab code on GitHub which helps automate the job of plotting posterior predictive distributions. If you are inferring posterior distributions of parameters of a 1D function (e.g. y=mx+c) then this code will plot the posterior predictive distribution for you. This should be handy for you to eyeball how well a model …

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## Book review: Bayes’ Rule, by James V Stone

Until recently, many texts on Bayesian inference assumed the reader had a strong background in mathematics or statistics. I found that really frustrating and it really got in my way of understanding this stuff. But this concise book (~160 pages) is a really great introduction. If I had this book when I was learning, …

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## Software for MCMC

If you’ve decided to join the increasing number of people using MCMC methods to conduct Bayesian inference, then one important decision is which software to use. This decision will be influenced by your programming language of choice, see Figure below. If you use Matlab, then really your best choice at the moment is JAGS. You use it …

## Combining prior beliefs and sensory evidence

If we want to try to locate a target of interest given a brief glimpse of a visual scene, then we can use at least two sources of information. Firstly, we can use any visual cues which give away the target’s location. However, in many cases the visual cues are insufficient to work out precisely …

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## Parallel processing of uncertain sensory information account for search asymmetry effects

The yes/no detection task is a classic method used to probe the inner workings of how humans process information. In this paper I was interested in one quite specific experimental phenomenon of visual information processing: that of search asymmetries. If you search for an item A amongst distracters B, then you will have some level …

## Optimal feature integration in visual search

One of the broad aims of my work is to apply the approach of Bayesian Decision Theory to attentional phenomena. In this particular paper, published back in 2009, I examined one specific aspect of the approach: the decision rule. Described very succinctly Bayesian Decision Theory consists of two main steps. Firstly, we make an inference about …

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