PRML 3.3.3-3.4 ベイズ線形回帰とモデル選択 / Baysian Linear Regression and Model Comparison)
Bayesian Nonparametrics入門-点過程と統計的機械学習の接点-
PRML 3.3.3-3.4 ベイズ線形回帰とモデル選択 / Baysian Linear Regression and Model Comparison)
Documentation for GPML Matlab Code
Documentation for GPML Matlab Code version 4.2 1) What? The code provided here originally demonstrated the main algorithms from Rasmussen and Williams: Gaussian Processes for Machine Learning. It has since grown to allow more likelihood functions, further inference methods and a flexible framework for specifying GPs. Other GP packages can be found here. The code is written by Carl Edward Rasmussen
Stat 547Q : Statistical Modeling with Stochastic Processes
Kingman's coalescent emerging from a Wright-Fisher model. Note also the connection with perfect sampling. General Instructor: Alexandre Bouchard-Côté Check latest updates: [HTML][RSS] Dates: February 28 - April 6, 2011 Time: MW 13:30 - 15:00 Place: LSK 301 Don't hesistate to contact me if you have questions: bouchard AT stat.ubc.ca Next office hour: Wednesday, 3/30, 3:00-4:00, at LSK 330 Template
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publications_posters_presentations_code[Finale's Research Website]