Duvenaud showed in Black-Box Stochastic Variational Inference in Five Lines of Python how to make use of the Python module autograd to easily code black-box variational inference introduced in Black Box Variational Inference by Ranganath et al.
I adapted his code to linear models with spike and slab prior.
You will need python 3 with autograd and matplotlib.
A succinct description of the model can be found here.
In short: We use black-box variational inference to find an approximation to the posterior over all parameters using optimization. The spike and slab prior introduces continuous and discrete random variables. To sample from the approximate posterior, we use the Gumbel-Max trick for the discrete variables and the reparameterization trick for the continuous variables.
For the generation of the dataset, we follow Bettencourt in Bayes Sparse Regression. The covariates are all independently distributed around zero with unit variance, and there is a population of both large, relevant slopes and small, irrelevant slopes. Moreover, the data are collinear, with more covariates than observations, which implies a non-identified likelihood. In details: We have M=200 covariates and N=100 noisy observations (variance of Gaussian noise is 1.). The probability that a covariate is large is Bernoulli(0.05).
Bettencourt showed that the Finnish horseshoe prior does a pretty good job. However, the model is involved and tuning parameters demands a certain level of expertise.
The spike and slab prior finds relevant slopes and set irrelevant slopes to 0, as depicted on the following figure:
In the large N regime (in the figure below N=4000), the fit is excellent:
Laurent de Vito
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