Student-t Processes as Alternatives to Gaussian Processes Supplementary Material In Appendix 1, we provide proofs of Lemmas and Corollaries from our paper. We describe the derivatives of the log marginal likelihood of t

Source URL: www.cs.cmu.edu

Learning Latent Groups with Hinge-loss Markov Random Fields Stephen H. Bach Bert Huang Lise Getoor University of Maryland, College Park, Maryland 20742, USA

Source URL: stephenbach.net

• Statistics
• Probability theory
• Graphical models
• Machine learning
• Statistical models
• Statistical theory
• Bayesian statistics
• Markov random field
• Latent variable
• Expectationmaximization algorithm
• Marginal likelihood
• Conditional random field

Inference, Models and Simulation for Complex Systems Lecture 5 Prof. Aaron Clauset 1

Source URL: tuvalu.santafe.edu

• Statistics
• Statistical theory
• Model selection
• Estimation theory
• Bayesian statistics
• Probability distributions
• Likelihood function
• Maximum likelihood estimation
• Likelihood-ratio test
• Akaike information criterion
• Marginal likelihood
• Bayes factor

Documentation Migrate VersionPeter Beerli

Source URL: popgen.sc.fsu.edu

• Bayesian statistics
• Estimation theory
• Statistical theory
• Statistical inference
• Marginal likelihood
• Probability theory
• Coalescent theory
• Likelihood function
• Bayesian inference
• Maximum likelihood estimation
• Bayes factor

CELEBRATING 30 YEARS doimksc © 2011 INFORMS Vol. 30, No. 1, January–February 2011, pp. 22–24 issn ! eissn 1526-548X ! 11 ! 3001 ! 0022

Source URL: www-stat.wharton.upenn.edu

• Independence
• Probability distributions
• Actuarial science
• Multivariate statistics
• Stable distributions
• Copula
• Correlation and dependence
• Normal distribution
• Multivariate normal distribution
• Joint probability distribution
• Bayesian inference
• Marginal likelihood

Improving the Accuracy of Demographic and Molecular Clock Model Comparison While Accommodating Phylogenetic Uncertainty Guy Baele,*,1 Philippe Lemey,1 Trevor Bedford,2 Andrew Rambaut,2 Marc A. Suchard,3,4,5 and Alexander

Source URL: bedford.io

• Statistical inference
• Bayesian statistics
• Statistical theory
• Estimation theory
• Probability distributions
• Coalescent theory
• Bias of an estimator
• Mixture model
• Bayesian inference
• Monte Carlo method
• Maximum likelihood estimation
• Marginal likelihood

16 Basic Bayesian Methods Mark E. Glickman and David A. van Dyk Summary In this chapter, we introduce the basics of Bayesian data analysis. The key ingredients to a

Source URL: www.glicko.net

• Bayesian statistics
• Bayesian inference
• Prior probability
• Beta distribution
• Likelihood function
• Statistical inference
• Normal distribution
• Confidence interval
• Posterior probability
• Conjugate prior
• Marginal likelihood

2015 IEEE International Conference on Robotics and Automation (ICRA) Washington State Convention Center Seattle, Washington, May 26-30, 2015 Self-tuning M-estimators G. Agamennoni, P. Furgale and R. Siegwart

Source URL: europa2.informatik.uni-freiburg.de

• Estimation theory
• Statistical theory
• M-estimators
• Statistical inference
• Bayesian statistics
• Maximum likelihood estimation
• Normal distribution
• Bayes estimator
• Efficiency
• Estimator
• Marginal likelihood
• operator

Marginal likelihood for parallel series Peter McCullagh Department of Statistics, University of Chicago, ChicagoDecember 18, 2007 Abstract

Source URL: www.stat.uchicago.edu

Statistica Sinica), MARGINAL LIKELIHOOD FOR DISTANCE MATRICES Peter McCullagh University of Chicago Abstract: A Wishart model is proposed for random distance matrices in which the

Source URL: www.stat.uchicago.edu