<--- Back to Details
First PageDocument Content
Statistics / Statistical theory / Estimation theory / Bayesian statistics / Statistical inference / Loss function / Markov decision process / Reinforcement learning / Exponential family / Confidence interval / Likelihood function / Conjugate prior
Date: 2015-07-20 20:08:36
Statistics
Statistical theory
Estimation theory
Bayesian statistics
Statistical inference
Loss function
Markov decision process
Reinforcement learning
Exponential family
Confidence interval
Likelihood function
Conjugate prior

JMLR: Workshop and Conference Proceedings vol 40:1–38, 2015 Thompson Sampling for Learning Parameterized Markov Decision Processes Aditya Gopalan

Add to Reading List

Source URL: jmlr.org

Download Document from Source Website

File Size: 483,51 KB

Share Document on Facebook

Similar Documents

Likelihood Function .... Method of Moments ...........

Likelihood Function .... Method of Moments ...........

DocID: 1vn7a - View Document

NOISE ROBUST SPEECH RECOGNITION USING GAUSSIAN BASIS FUNCTIONS FOR NON-LINEAR LIKELIHOOD FUNCTION APPROXIMATION Chris Pal½ ¾ , Brendan Frey½ ¾ and Trausti Kristjansson ½ ¾ ½  ¾

NOISE ROBUST SPEECH RECOGNITION USING GAUSSIAN BASIS FUNCTIONS FOR NON-LINEAR LIKELIHOOD FUNCTION APPROXIMATION Chris Pal½ ¾ , Brendan Frey½ ¾ and Trausti Kristjansson ½ ¾ ½ ¾

DocID: 1uuy9 - View Document

The shape of the one-dimensional phylogenetic likelihood function

The shape of the one-dimensional phylogenetic likelihood function

DocID: 1u9Og - View Document

Is Bayes posterior just quick and dirty confidence? D.A.S. Fraser March 9, 2009 Abstract Bayesintroduced the observed likelihood function to statistical inference and provided a weight function to calibrate the p

Is Bayes posterior just quick and dirty confidence? D.A.S. Fraser March 9, 2009 Abstract Bayesintroduced the observed likelihood function to statistical inference and provided a weight function to calibrate the p

DocID: 1t5Vr - View Document

On the Application of Automatic Differentiation to the Likelihood Function for Dynamic General Equilibrium Models

On the Application of Automatic Differentiation to the Likelihood Function for Dynamic General Equilibrium Models

DocID: 1ryLK - View Document