The expectation-maximization (EM) algorithm for maximizing likelihood functions, combined with the Viterbi algorithm, is applied to the problem of sequence detection when symbol timing information is ...

This paper proposes a distributed multi round auction algorithm FMMRA that maximizes task cost-effectiveness, in response to the current application of auction algorithms to solve multi-agent task ...

Unsupervised learning techniques play a pivotal role in unraveling protein folding landscapes, constructing Markov State Models, expediting replica exchange simulations, and discerning drug binding ...

This paper surveys a short study about using and applying symbolic processing to direct execution of the expectation-maximization algorithm. Formulas are derived in the manner defined by the ...

Free-energy perturbation-based relative binding free-energy (FEP-RBFE) calculations have become an important tool in drug discovery, but inherent computational errors require corrections based on ...