A short viewpoint piece that Keith and I wrote just came out in Perception. Go check it out, it’s open access. May, K. A., & Vincent, B. T. (2016). Fewer Statistical Tests … or Better Ones? Perception. http://doi.org/10.1177/0301006616677909

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## Bayesian analysis toolbox for delay discounting, version 1.3

Posterior predictive checks The toolbox now calculates 2 measures of “goodness of fit” of the models. This is a useful quantitative reassurance that the models describe the participant discounting behaviour better than chance. In turn, this is important when we come to deciding which (if any) data files we should exclude. You can go and […]

Continue readingMore Tag## Bayesian analysis toolbox for delay discounting, version 1.2

I’ve just released Version 1.2 of the toolbox ‘Bayesian analysis toolbox for delay discounting.’ The main feature of this release was the addition of new models. For example, you can now estimate discount rates (ignoring the magnitude effect). So you can obtain estimates of the discount rate k, which is very useful if your primary […]

Continue readingMore Tag## Probabilistic programming: a tentative first encounter

While I have some experience with probabilistic programming in the flavour of Bayesian Networks, and have published papers using them, I am interested in the super-class of generic probabilistic programs. That is, right now I am happy with conducting inference on Bayesian Networks, but I want to learn how to conduct inference on generic programs. As […]

Continue readingMore Tag## Importance sampling Matlab demo

Importance sampling is related to rejection sampling, which I looked at in the last post. Here is a short demo. A problem of rejection sampling is that many samples could be evaluated in regions of low probability mass. This then lead to a high rate of attrition, with many samples being rejected. In importance sampling, this seems […]

Continue readingMore Tag## 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.

Continue readingMore Tag## A tutorial on Bayesian models of perception

Vincent, B. T. (2015) A tutorial on Bayesian models of perception, Journal of Mathematical Psychology, 66:103–114.

Continue readingMore Tag## 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 […]

Continue readingMore Tag## 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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