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A Mixture of Delta-Rules Approximation to Bayesian Inference in Change-Point Problems

Figure 2

An example of the generative process behind a change-point data set with Gaussian data.

(A) First, the change-point locations (grey lines) are sampled from a Bernoulli process with known hazard rate (in this case, ). (B) Next, the mean of the Gaussian distribution, , is sampled from the prior distribution defined by parameters and , , (C) for each epoch between change-points (in this case, and ). (D) Finally, the data points at each time step () are sampled from a Gaussian distribution with the current mean and a variance of 1, , shown in (E) for the mean of the last epoch.

doi: https://doi.org/10.1371/journal.pcbi.1003150.g002