STATISTICS Dalarna University D-level Master’s Thesis 2007 Which estimator of the dispersion parameter for the Gamma family

Source URL: www.statistics.du.se

• Statistical inference
• Maximum likelihood
• Estimator
• Normal distribution
• Bias of an estimator
• Gamma distribution
• Consistent estimator
• Generalized linear model
• Scale parameter
• Statistics
• Estimation theory
• Statistical theory

arXiv:1401.2838v1 [cs.LG] 13 JanGPS-ABC: Gaussian Process Surrogate Approximate Bayesian Computation Max Welling

Source URL: www.ics.uci.edu

• Statistical inference
• Econometrics
• Confidence interval
• Measurement
• Marginal likelihood
• Likelihood function
• Normal distribution
• Markov chain
• Maximum likelihood
• Statistics
• Estimation theory
• Bayesian statistics

Some Determinants of Cost Efficiency in German Public Transport Matthias Walter∗ 22nd July 2009 Submission to the 11TH CONFERENCE ON COMPETITION AND OWNERSHIP IN LAND TRANSPORT

Source URL: ses.library.usyd.edu.au

• Regression analysis
• Stochastic frontier analysis
• Data envelopment analysis
• Macroeconomics
• Inefficiency
• Economic model
• Econometrics
• Macroeconomic model
• Maximum likelihood
• Economics
• Estimation theory
• Statistics

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

MASTER THESIS IN MICRODATA ANALYSIS Modelling S shaped hazard function A case of evaluating Volvo Cars training project Author:

Source URL: www.statistics.du.se

• Rectangular function
• Mathematics
• Survival analysis
• Statistical models
• M-estimators
• Estimation theory
• Statistics
• Likelihood function

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

On October 16th, 6 to 8 p.m.,Joshua Liner Gallery (540 West 28th Street) opens two new shows: “Qualia” by Tiffany Bozic and “Likelihood of Confusion” by Alfred Steiner. For the former, Bozic “explores the notio

Source URL: joshualinergallery.com

Exploiting Inference for Approximate Parameter Learning in Discriminative Fields: An Empirical Study Sanjiv Kumar, Jonas August, and Martial Hebert The Robotics Institute, Carnegie Mellon University Pittsburgh, PA 15213

Source URL: www.sanjivk.com

• Graphical models
• Bayesian statistics
• Probability theory
• Maximum likelihood
• Likelihood function
• Conditional random field
• Belief propagation
• Statistics
• Estimation theory
• M-estimators

The Randomness of the Real World In the real-world, there are many random (formally, stochastic) systems changing over time which can be modelled very accurately through sequences of dependent random variables than indep

Source URL: eagle-essays.com

• Stochastic processes
• Estimation theory
• Markov chain
• Random walk
• Bioinformatics
• Stochastic matrix
• Andrey Markov
• Maximum likelihood
• Likelihood function
• Statistics
• Markov models
• Markov processes

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