Results 41 to 50 of about 1,347,426 (185)

Bottom-up learning of hierarchical models in a class of deterministic POMDP environments

International Journal of Applied Mathematics and Computer Science, 2015
The theory of partially observable Markov decision processes (POMDPs) is a useful tool for developing various intelligent agents, and learning hierarchical POMDP models is one of the key approaches for building such agents when the environments of the ...
Itoh Hideaki, Fukumoto Hisao, Wakuya Hiroshi, Furukawa Tatsuya   +3 more
doaj   +1 more source

Bayesian Hierarchical Random Effects Models in Forensic Science

Frontiers in Genetics, 2018
Statistical modeling of the evaluation of evidence with the use of the likelihood ratio has a long history. It dates from the Dreyfus case at the end of the nineteenth century through the work at Bletchley Park in the Second World War to the present day.
Colin G. G. Aitken
doaj   +1 more source

Hierarchical Character-Word Models for Language Identification

, 2016
Social media messages' brevity and unconventional spelling pose a challenge to language identification. We introduce a hierarchical model that learns character and contextualized word-level representations for language identification. Our method performs
Hathi, Shobhit, Jaech, Aaron, Mulcaire, George, Ostendorf, Mari, Smith, Noah A.   +4 more
core   +1 more source

Supersymmetry Breaking in Warped Geometry [PDF]

, 2003
We examine the soft supersymmetry breaking parameters in supersymmetric theories on a slice of AdS_5 which generate the hierarchical Yukawa couplings by dynamically localizing the bulk matter fields in extra dimension.
a, Altendorfer, Arkani-Hamed, Arkani-Hamed, Casas, Ceresole, Chacko, Chacko, Choi, Choi, Chun, D. Y. Kim, Grossman, Hebecker, Huber, I.-W. Kim, K. Choi, Kaplan, Karch, Kubo, Linch, Luty, Marti, Marti, Nelson, Randall, Randall, T. Kobayashi   +27 more
core   +1 more source

Hierarchical Models for Relational Event Sequences [PDF]

, 2012
Interaction within small groups can often be represented as a sequence of events, where each event involves a sender and a recipient. Recent methods for modeling network data in continuous time model the rate at which individuals interact conditioned on ...
Butts, Carter T., DuBois, Christopher, McFarland, Daniel, Smyth, Padhraic   +3 more
core   +1 more source

Hierarchical Species Sampling Models [PDF]

Bayesian Analysis, 2020
This paper introduces a general class of hierarchical nonparametric prior distributions. The random probability measures are constructed by a hierarchy of generalized species sampling processes with possibly non-diffuse base measures. The proposed framework provides a general probabilistic foundation for hierarchical random measures with either atomic ...
Bassetti F., Casarin R., Rossini L.
openaire   +5 more sources

Bambi: A Simple Interface for Fitting Bayesian Linear Models in Python

Journal of Statistical Software, 2022
The popularity of Bayesian statistical methods has increased dramatically in recent years across many research areas and industrial applications. This is the result of a variety of methodological advances with faster and cheaper hardware as well as the ...
Tomás Capretto, Camen Piho, Ravin Kumar, Jacob Westfall, Tal Yarkoni, Osvaldo A Martin   +5 more
doaj   +1 more source

Monitoring Animal Populations With Cameras Using Open, Multistate, N‐Mixture Models

Ecology and Evolution
Remote cameras have become a mainstream tool for studying wildlife populations. For species whose developmental stages or states are identifiable in photographs, there are opportunities for tracking population changes and estimating demographic rates ...
Alexej P. K. Sirén, Michael T. Hallworth, Jillian R. Kilborn, Chris A. Bernier, Nicholas L. Fortin, Katherina D. Geider, Riley K. Patry, Rachel M. Cliché, Leighlan S. Prout, Suzanne J. Gifford, Scott Wixsom, Toni Lyn Morelli, Tammy L. Wilson   +12 more
doaj   +1 more source

dalmatian: A Package for Fitting Double Hierarchical Linear Models in R via JAGS and nimble

Journal of Statistical Software, 2021
Traditional regression models, including generalized linear mixed models, focus on understanding the deterministic factors that affect the mean of a response variable.
Simon Bonner, Han-Na Kim, David Westneat, Ariane Mutzel, Jonathan Wright, Matthew Schofield   +5 more
doaj   +1 more source

Methods of Hierarchical Clustering [PDF]

, 2011
We survey agglomerative hierarchical clustering algorithms and discuss efficient implementations that are available in R and other software environments. We look at hierarchical self-organizing maps, and mixture models.
Contreras, Pedro, Murtagh, Fionn
core   +2 more sources