Fast Bayesian Inference of Copy Number Variants using Hidden Markov Models with Wavelet Compression
Fig 7
Mapping of wavelets ψj, k and data points yt to tree nodes Nℓ, t.
Each node is the root of a subtree with n = 2ℓ leaves; pruning that subtree yields a block of size n, starting at position t. For instance, the node N1,6 is located at position 13 of the DFS array (solid line), and corresponds to the wavelet ψ3,3. A block of size n = 2 can be created by pruning the subtree, which amounts to advancing by 2n − 1 = 3 positions (dashed line), yielding N3,8 at position 16, which is the wavelet ψ1,1. Thus the number of steps for creating blocks per iteration is at most the number of nodes in the tree, and thus strictly smaller than 2T.