Probabilistic models, such as hidden Markov models or Bayesian networks, are commonly used to model biological data. Much of their popularity can be attributed to the existence of efficient and robust ...

This package contains functions implementing the BrainMap model proposed in Mejia et al. (2019) and the spatial BrainMap model proposed in proposed in Mejia et al. (2020+). (Previously, these models ...

Abstract: Direction-of-arrival (DOA) estimation for wideband source signals using far-field acoustic sensors has recently drawn much research interest. A wide variety of DOA estimation approaches are ...

Division of Intramural Research, National Library of Medicine, National Institutes of Health, Bethesda, Maryland 20894, United States ...

This paper shows that the Expectation-Maximization (EM) algorithm for regime-switching dynamic factor models provides satisfactory performance relative to other estimation methods and delivers a good ...

ABSTRACT: Compositional data, such as relative information, is a crucial aspect of machine learning and other related fields. It is typically recorded as closed data or sums to a constant, like 100%.

ABSTRACT: This paper is concerned about studying modeling-based methods in cluster analysis to classify data elements into clusters and thus dealing with time series in view of this classification to ...

Planar motion constraint occurs in visual odometry (VO) and SLAM for Automated Guided Vehicles (AGVs) or mobile robots in general. Conventionally, two-point solvers can be nested to RANdom SAmple ...

To solve the low efficiency of simple greedy algorithms, [22] in 2009 proposed a degree discount heuristics to improve influence spread, by considering the degree discount of a candidate node caused ...

The best algorithm for a computational problem generally depends on the “relevant inputs,” a concept that depends on the application domain and often defies formal articulation. Although there is a ...