Fast Bayesian Inference of Copy Number Variants using Hidden Markov Models with Wavelet Compression
Fig 1
Instead of individual computations per observation (panel a), Forward-Backward Gibbs Sampling is performed on a compressed version of the data, using sufficient statistics for block-wise computations (panel b) to accelerate inference in Bayesian Hidden Markov Models. During the sampling (panel c) parameters and copy number sequences are sampled iteratively. During each iteration, the sampled emission variances determine which coefficients of the data’s Haar wavelet transform are dynamically set to zero. This controls potential break points at finer or coarser resolution or, equivalently, defines blocks of variable number and size (panel c, bottom). Our approach thus yields a dynamic, adaptive compression scheme which greatly improves speed of convergence, accuracy and running times.