Uncertainty  in  Natural  Intelligence  Workshop  at  UAI  2012   8:20-­‐8:30   Introduction  

Source URL: cocosci.berkeley.edu

• Statistical theory
• Statistics
• Philosophy of science
• Statistical inference
• Bayesian statistics
• Philosophy
• Bayesian
• Bayesian inference
• Prior probability
• Causality

University of California, Irvine Statistics Seminar Bayesian Inference for Latent Biologic Structure with Determinantal Point Processes (DPP) Peter Mueller Professor

Source URL: www.stat.uci.edu

Penn State Astrostatistics MCMC tutorial Murali Haran, Penn State Dept. of Statistics Bayesian change point model with Gamma hyperpriors Consider the following hierarchical changepoint model for the number of occurrences

Source URL: sites.stat.psu.edu

THE SPEED OF SOCIAL LEARNING MATAN HAREL, ELCHANAN MOSSEL, PHILIPP STRACK, AND OMER TAMUZ Abstract. We consider two Bayesian agents who learn from exogenously provided private signals, as well as the actions of the other

Source URL: people.hss.caltech.edu

• Estimation theory
• Statistical theory
• Statistics
• Bayesian statistics
• Likelihood function
• Statistical inference

The Meaning of Events Ashish Mahabal, S.G. Djorgovski, Ciro Donalek California Institute of Technology The classification of complex celestial events Exploration of the time domain is rapidly becoming one of the most exc

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• Statistics
• Machine learning
• Statistical classification
• Pattern recognition
• Bayesian inference
• Catalina Real-time Transient Survey
• Artificial neural network
• Bayesian network
• Prior probability
• Supervised learning
• Regression analysis
• Time series

Empirical Likelihood Test for High Dimensional Linear Models Liang Peng1 , Yongcheng Qi2 and Ruodu Wang3 January 7, 2014 Abstract We propose an empirical likelihood method to test whether the coefficients in a possibly h

Source URL: sas.uwaterloo.ca

• Statistics
• Statistical theory
• Estimation theory
• Regression analysis
• Probability distributions
• Parametric statistics
• Bayesian statistics
• Normal distribution
• Linear regression
• Likelihood function

BAYESIAN COMPRESSIVE SENSING OF WAVELET COEFFICIENTS USING MULTISCALE LAPLACIAN PRIORS Esteban Vera1 , Luis Mancera2 , S. Derin Babacan3 , Rafael Molina2 , and Aggelos K. Katsaggelos3 1 2

Source URL: decsai.ugr.es

• Signal processing
• Electrical engineering
• Statistics
• Mathematical analysis
• Wavelet
• Compressed sensing
• Bayesian network

The Annals of Applied Statistics 2014, Vol. 8, No. 2, 1256–1280 DOI: AOAS739 © Institute of Mathematical Statistics, 2014 PROBABILITY AGGREGATION IN TIME-SERIES: DYNAMIC

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

• Probability
• Statistics
• Statistical theory
• Bayesian statistics
• Sampling
• Survey methodology
• Kalman filter
• Calibrated probability assessment
• Probability distribution
• Brier score
• Mathematical statistics
• Prior probability

Stat Biosci:206–224 DOIs12561Disease Diagnosis from Immunoassays with Plate to Plate Variability: A Hierarchical Bayesian Approach Oliver A. Entine · Dylan S. Small · Shane T. Jensen ·

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

• Probability distributions
• Bayesian statistics
• Beta distribution
• Summary statistics
• Mixture model
• Normal distribution
• Prior probability
• Likelihood function
• Mode

Markov Chain Monte Carlo and Variational Inference: Bridging the Gap Tim Salimans Algoritmica TIM @ ALGORITMICA . NL

Source URL: jmlr.org

• Statistics
• Statistical theory
• Probability
• Markov models
• Computational statistics
• Bayesian statistics
• Monte Carlo methods
• Monte Carlo software
• Variational Bayesian methods
• Bayesian network
• Markov chain
• Gibbs sampling