Estimation of affine term structure models with spanned or unspanned stochastic volatility

Source URL: faculty.chicagobooth.edu

• Mathematical finance
• Bayesian statistics
• Maximum likelihood
• Expectation–maximization algorithm
• Likelihood-ratio test
• Stochastic volatility
• Autoregressive conditional heteroskedasticity
• Likelihood function
• Statistics
• Estimation theory
• Statistical theory

THIS IS A DRAFT VERSION. FINAL VERSION TO BE PUBLISHED AT NIPS ’06 Structure Learning in Markov Random Fields Sridevi Parise Bren School of Information and Computer Science

Source URL: www.ics.uci.edu

• Probability theory
• Estimation theory
• Graphical models
• Marginal likelihood
• Expectation–maximization algorithm
• Variational Bayesian methods
• Normal distribution
• Maximum likelihood
• Bayesian network
• Statistics
• Bayesian statistics
• Statistical theory

Evidence Estimation for Bayesian Partially Observed MRFs Yutian Chen Department of Computer Science University of California, Irvine Irvine, CA 92697

Source URL: www.ics.uci.edu

• Normal distribution
• Expectation–maximization algorithm
• Variational Bayesian methods
• Bayesian network
• Markov chain Monte Carlo
• Maximum likelihood
• Deviance information criterion
• Pierre-Simon Laplace
• Markov random field
• Statistics
• Bayesian statistics
• Marginal likelihood

Bayesian Posterior Sampling via Stochastic Gradient Fisher Scoring Sungjin Ahn Dept. of Computer Science, UC Irvine, Irvine, CA, USA SUNGJIA @ ICS . UCI . EDU

Source URL: www.ics.uci.edu

• Fisher information
• Markov chain Monte Carlo
• Normal distribution
• Stochastic gradient descent
• Maximum a posteriori estimation
• Confidence interval
• Central limit theorem
• Score
• Expectation–maximization algorithm
• Statistics
• Estimation theory
• Maximum likelihood

Collapsed Variational Dirichlet Process Mixture Models∗ Max Welling Kenichi Kurihara Dept. of Computer Science Dept. of Computer Science UC Irvine, USA

Source URL: www.ics.uci.edu

• Expectation–maximization algorithm
• Dirichlet process
• Mixture model
• Latent Dirichlet allocation
• Normal distribution
• Factorial
• Markov chain
• Central limit theorem
• Bayesian inference
• Statistics
• Bayesian statistics
• Variational Bayesian methods

Evaluation of a New Variance Component Estimation Method Hierarchical GLM Approach with Application in QTL Analysis Author:

Source URL: www.statistics.du.se

• Data analysis
• Bayesian statistics
• Statistical theory
• Generalized linear model
• Maximum likelihood
• Expectation–maximization algorithm
• Normal distribution
• Mixed model
• Restricted maximum likelihood
• Statistics
• Estimation theory
• Regression analysis

CS229 Lecture notes Andrew Ng Mixtures of Gaussians and the EM algorithm In this set of notes, we discuss the EM (Expectation-Maximization) for density estimation. Suppose that we are given a training set {x(1) , . . . ,

Source URL: see.stanford.edu

Estimating Gaussian Mixture Densities with EM – A Tutorial Carlo Tomasi – Duke University Expectation Maximization (EM) [4, 3, 6] is a numerical algorithm for the maximization of functions of several variables. There

Source URL: www.cs.duke.edu

Privacy-Preserving Reconstruction of Multidimensional Data Maps in Vehicular Participatory Sensing Nam Pham1 , Raghu K. Ganti1 , Yusuf S. Uddin1 , Suman Nath2 and Tarek Abdelzaher1 1

Source URL: research.microsoft.com

• Maximum likelihood
• Expectation–maximization algorithm
• Likelihood function
• Perturbation theory
• Statistics
• Estimation theory
• Mathematical analysis

Latent Variable Models for Predicting File Dependencies in Large-Scale Software Development Diane J. Hu1 , Laurens van der Maaten1,2 , Youngmin Cho1 , Lawrence K. Saul1 , Sorin Lerner1 1 Dept. of Computer Science & Engin

Source URL: lvdmaaten.github.io

• Mixture model
• Expectation–maximization algorithm
• Dirichlet process
• Bernoulli distribution
• Variational Bayesian methods
• Statistics
• Cluster analysis
• Machine learning