Identifying interpretable patterns in multi-electrode recordings is a longstanding and increasingly pressing challenge in neuroscience.

Moreover, the provable recombination algorithm is slower than an LP solver implementation.

To measure the difference between two probability distributions, referred to as the source and target, respectively, we exploit both the chain rule and Bayes' theorem to construct conditional transport (CT), which is constituted by both a

Haplotype assembly and viral quasispecies reconstruction are challenging tasks concerned with analysis of genomic mixtures using sequencing data.

In the standard MLE setting [see, e.g., Murphy, 2012, Ch. 9] we are interested in learning the These two definitions are, however, essentially equivalent. Eq. (15) is a smooth objective that can be optimized with a (stochastic) gradient descent procedure. This section contains the proofs of the results relative to the perturb and map section (Section 3.2) and The proposition now follows from arguments made in Papandreou and Y uille [2011] Its moment generating function has the form E[exp(tX)] = Γ(1 τt). As mentioned in Johnson and Balakrishnan [p. Parts of the proof are inspired by a post on stackexchange Xi'an [2016].Theorem 1.