mcmc_heatmaps
MCMC mean graphs
Module |
Description
For Bayesian inference it is custom to use MCMC methods to simulate a Markov chain of graphs \(\{G^l\}_{l=0}^\infty\) having the graph posterior as stationary distribution. Suppose we have a realisation of length \(M + 1\) of such chain, then the posterior edge probability of an edge e is estimated by \(\frac{1}{M+1-b} \sum_{l=b}^{M} \mathbf{1}_{e}(e^l)\), where the first \(b\) samples are disregarded as a burn-in period.
This module has a list of objects, where each object has
Fields
burn_in
percent [0, 1] to burn of the number of samples.
The estimated probabilities are plotted in heatmaps using seaborn which are saved in results/mcmc_heatmaps/ and copied to results/output/mcmc_heatmaps/ for easy reference.
Example
[
{
"id": "omcmc_itsample-bge",
"burn_in": 0,
"active": true
}
]