A Dirichlet process mixture of hidden Markov models for protein structure prediction

DOI10.1214/09-AOAS296zbMath1194.62117arXiv1011.2065OpenAlexW3103426803WikidataQ30395528 ScholiaQ30395528MaRDI QIDQ993267

Ryan Day, Jerry W. Tsai, David B. Dahl, Marina Vannucci, Kristin P. Lennox

Publication date: 10 September 2010

Published in: The Annals of Applied Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1011.2065

zbMATH Keywords

Bayesian nonparametricsvon Mises distributiondensity estimationprotein structure predictiontorsion anglesdihedral angles

Mathematics Subject Classification ID

Density estimation (62G07) Applications of statistics to biology and medical sciences; meta analysis (62P10) Bayesian inference (62F15) Biochemistry, molecular biology (92C40)

Related Items

Modeling angles in proteins and circular genomes using multivariate angular distributions based on multiple nonnegative trigonometric sums ⋮ Recent advances in directional statistics ⋮ Unnamed Item ⋮ Bayesian analysis for bivariate von Mises distributions ⋮ Non-parametric Bayesian inference for continuous density hidden Markov mixture model ⋮ Bayesian nonparametrics for directional statistics ⋮ Circular local likelihood ⋮ Unsupervised classification and analysis of objects described by nonparametric probability distributions ⋮ Dirichlet process and its developments: a survey

Uses Software

• PSIPRED
• MUSCLE

Cites Work

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