Dirichlet Processes: Tutorial and Practical Course

Published on Aug 27, 2007140397 Views

Yee Whye Teh

**The Bayesian approach** allows for a coherent framework for dealing with uncertainty in machine learning. By integrating out parameters, Bayesian models do not suffer from overfitting, thus it is co

MLSS 2007 - Tübingen

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Dirichlet Processes

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Chapter list

Dirichlet Processes: Tutorial and Practical Course00:01

Dirichlet Processes01:34

Outline-part0103:55

Outline-part0204:34

Function estimation-part0104:37

Function estimation-part0206:42

Function estimation-part0307:41

Density estimation-part0108:08

Density estimation-part0210:11

Density estimation-part0311:08

Density estimation-part0411:26

Density estimation-part0513:26

Density estimation-part0614:40

Semiparametric modelling-part0115:26

Semiparametric modelling-part0217:55

Model selection/averaging-part0119:47

Model selection/averaging-part0221:34

Model selection/averaging-part0321:41

Model selection/averaging-part0422:03

Model selection/averaging-part0523:54

Model selection/averaging-part04A24:05

Model selection/averaging-part05A28:10

Model selection/averaging-part0628:35

Model selection/averaging-part0730:25

Model selection/averaging-part0831:32

Model selection/averaging-part0933:44

Outline-part0334:36

Finite mixture models34:52

Infinite mixture models36:15

Gaussian processes-part0139:46

Gaussian processes-part0240:19

Gaussian processes-part0341:10

Dirichlet processes-part0141:45

Dirichlet processes-part0243:25

Dirichlet processes-part01A43:33

Dirichlet processes-part02A43:47

Dirichlet processes-part0345:44

Dirichlet processes-part0447:09

Dirichlet processes-part0548:25

Dirichlet processes-part0651:34

Dirichlet processes-part0754:28

Dirichlet processes-part0855:28

Dirichlet processes-part0956:23

Dirichlet processes-part1056:46

Dirichlet processes-part1157:38

Dirichlet processes-part1257:56

Dirichlet processes-part1358:13

Outline-part0458:16