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 […]

# Category archives: Research Code

## 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 […]

## Slice sampling Matlab demo

So far we’ve had a look at rejection sampling and importance sampling. Here we take a quick look at slice sampling, although rather than implementing it ourselves, we will use the built in Matlab slicesample function. Using our parameter estimation example, we will use slice sampling to estimate the mean and sigma of some samples from […]

## useful tree command on mac

I found a simple way to produce tree-like listings of files/folders on the mac. This is a very useful tool to get an overview of some project code. You can install it simply (following instructions I found here). First, open a terminal and install Homebrew if you do not have it already. It’s a package […]

## Importance sampling Matlab demo

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 […]

## Rejection sampling Matlab demo

I’ve been using MCMC, but I’ve wanted to flesh out my knowledge and explore the space of sampling approaches a little more. One very simple, yet inefficient method, is rejection sampling. Here is a little Matlab example I put together after seeing how easy it was. %% true probability distribution true_func = @(x) betapdf(x,1+1,1+10); %% Do […]

## Pimp your research code using UML class diagrams

Ideally, all research code should be made available at the point of submitting a paper. I’ve found that the way I write my research code has changed for the better now that I’ve made a commitment to making it open. However it can somewhat opaque and time consuming to understand, so how can we help those wanting to review, use, […]

## 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 […]

## Energy efficient receptive field code

[UPDATE: February 2014] The files and instructions are now hosted on GitHub. Energy efficient receptive field code by Benjamin Vincent is licensed under a Creative Commons Attribution-Non-Commercial-Share Alike 3.0 Unported License. This minimal set of MATLAB functions will set up a simple neural network to learn receptive fields. These receptive fields minimise an energy function which involves a) […]