MAP inference algorithms without approximation for collective graphical models on path graphs via discrete difference of convex algorithm

Collective graphical model (CGM) is a probabilistic model that provides a framework for analyzing aggregated count data. Maximum a posteriori (MAP) inference of unobserved variables under given observations is one of the essential operations in CGM. Because the MAP inference problem is known to be N...

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Vydané v:	Machine learning Ročník 112; číslo 1; s. 99 - 129
Hlavní autori:	Akagi, Yasunori, Marumo, Naoki, Kim, Hideaki, Kurashima, Takeshi, Toda, Hiroyuki
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York Springer US 01.01.2023 Springer Nature B.V
Predmet:	Algorithms Approximation Artificial Intelligence Computer Science Continuity (mathematics) Control Graphs Inference Machine Learning Mechatronics Natural Language Processing (NLP) Probabilistic models Robotics Simulation and Modeling Network flow Collective graphical model Discrete difference of convex algorithm
ISSN:	0885-6125, 1573-0565
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