Project: Box2D_friction_mod      WheelConstraint.h      box2d_friction_joint.patch      b2FrictionJoint.h      TopDownCar.h      b2FrictionJoint.cpp      TestEntries.cpp      python_friction_joint.patch     

Project: jvfeatures

jvtypes.h      jvtypes.cpp      jvfeatures.h      chessSeg.cpp      jvtest.cpp      jvfeatures.cpp     

Project: Other


Project: Infinite HMM Tutorial

HMMProblem.m      HMM.m      README.txt      HDP.m      HDP_HMM.m      run.m      ConditionalProbabilityTable.m     

Project: Arduino_Code

arduino-serial.c      oscilloscope.pde      ranger_test.pde      accelerometer_test.pde      pwm_manual.pde      servo_test.pde      motordriver.pde      ranger_plane_sweep.pde      helicopter_controller.pde      clodbuster_controller.pde     

Project: RRT

BidirectionalRRT.cpp      RRT.tgz      AbstractRRT.cpp      RRT.h      RRT.cpp      rrt_test.cpp     

Project: ArduCom     

Project: Dirichlet Process Mixture Tutorial

EM_GM.m      DP_Demo.m      DirichletProcess.m      DPMM.m      gaussian_EM.m     

Project: support

Protector.php      geshi.php     

Project: Cogent

CodePane.php      Cogent.php      NotesPane.php      PubsTable.php      PicsPane.php     
Click here to download "resources/code/Infinite HMM Tutorial/README.txt"

resources/code/Infinite HMM Tutorial/README.txt

iHMM Demo Library

This directory contains code for training fixed and variable-sized HMM
models. The implementation and example are based on the infinite Hidden
Markov Model, presented at NIPS in 2003 by Beal et al.  

This is NOT intended for use as an actual sampler for the iHMM - it's
alpha and not remotely game-ready.  It was written as an exercise and to
share it with my lab group at Georgia Tech.  After that meeting I had
little excuse to work out the kinks.  One notable omission is the
resampling of alpha and beta (the HDP concentration params) in the iHMM
example, which I have stubs for but never finished.  

* Also, in a moment of insanity I refactored the code into a fully OOP
implementation, which seems inadvisable in Matlab.  Factoring this way
made the code more readable for me, but it seemed to run far slower than
the imperative version (I blame the GC).  

** If you're looking for a usable, turn-key implementation, I suggest you
check out Jurgen Van Gael's site:

Jonathan Scholz

About me

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