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Subsections
Alphabetical list of functions
- c_dgaus
- Computes a set of multivariate normal density values in the
case of diagonal covariance matrices (mex-file).
- gauselps
- Plots 2D projections of Gaussian ellipsoids.
- gauseval
- Computes a set of multivariate normal density values.
- gauslogv
- Computes a set of multivariate normal log-density values.
- hmm
- Performs multiple iterations of the EM algorithm.
- hmm_chk
- Checks the parameters of an HMM and returns its dimensions.
- hmm_dens
- Reestimates the Gaussian parameters for an HMM.
- hmm_fb
- Implements the forward-backward recursion (with scaling).
- hmm_gen
- Generates a sequence of observation given an HMM.
- hmm_mest
- Reestimates the transition parameters for multiple observation
sequences.
- hmm_mint
- Initializes the distribution parameters using multiple
observations (left-right model).
- hmm_psim
- Generates a random sequence of conditional HMM states.
- hmm_tran
- Reestimates the transition part of an HMM.
- hmm_vit
- Computes the most likely sequence of states (Viterbi DP
algorithm).
- lrhmm
- Performs multiple iterations of the EM algorithm for a left-right
model.
- mix
- Performs multiple iterations of the EM algorithm for a mixture
model.
- mix_chk
- Checks the parameters of a mixture model and return its
dimensions.
- mix_gen
- Generates a sequence of observation for a Gaussian mixture
model.
- mix_par
- Reestimates mixture parameters.
- mix_post
- Computes a posteriori probabilities for a Gaussian mixture
model.
- randindx
- Generates random indexes with a specified probability
distribution.
- statdis
- Returns the stationary distribution of a Markov chain.
- svq
- Vector quantization using successive binary splitting steps.
- vq
- Vector quantization using the K-means (or LBG) algorithm.
The main functions are described in section 2.2 (or in the example
scripts), other functions include:
hmm_gen
and mix_gen
generate data vectors according to a given
model. This is useful for testing algorithms on ``prototype data''.
hmm_psim
generates a random sequence of HMM state conditional to an
observation sequence. This can be used for doing Monte Carlo simulations (the
way it works is described, for instance, in [10] as
``sampling the indicator variables'').
gauseval
and gauslogv
compute values of the Gaussian probability
density (or the logarithm of it for gauslogv
) for several Gaussian
distributions and several observed vectors at the same time. Computing as many
values as possible at the same time is much faster than calling the function
several times (especially when the number of Gaussian distributions is large).
gauselps
plots the 2-D projections of the Gaussian ellipsoids
corresponding to the Gaussian distribution (this is certainly one of the most
useful things in order to see what's going on, at least for low dimensional
models).
statdis
computes the stationary distribution of a finite state-space
Markov chain from its transition matrix.
Next: Functions in the H2M/cnt
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Olivier Cappé, Aug 24 2001