ReadMe file for the NOISE toolbox version 0.1 Saturday, June 12, 2004 at 14:21:33
Written by Neil D. Lawrence.$Revision: 1.3 1.0 $

cmpndLikelihood.m: Likelihood of data under compound noise model.
cmpndLogLikelihood.m: Log-likelihood of data under compound noise model.
cmpndNoiseDisplay.m: Display the parameters of the compound noise model.
cmpndNoiseExpandParam.m: Expand probit noise structure from param vector.
cmpndNoiseExtractParam.m: Extract parameters from compound noise model.
cmpndNoiseGradVals.m: Gradient wrt x of log-likelihood for compound noise model.
cmpndNoiseGradientParam.m: Gradient of the compound noise model's parameters.
cmpndNoiseNuG.m:  Update nu and g parameters associated with compound noise model.
cmpndNoiseOut.m: Output from compound noise model.
cmpndNoiseParamInit.m: Compound noise model's parameter initialisation.
cmpndNoiseSites.m: Site updates for compound noise model.
gaussianLikelihood.m: Likelihood of data under Gaussian noise model.
gaussianLogLikelihood.m: Log-likelihood of data under Gaussian noise model.
gaussianNoiseDisplay.m: Display the parameters of the Gaussian noise model.
gaussianNoiseExpandParam.m: Expand Gaussian noise structure from param vector.
gaussianNoiseExtractParam.m: Extract parameters from Gaussian noise model.
gaussianNoiseGradVals.m: Gradient wrt mu and varsigma of log-likelihood for gaussian noise model.
gaussianNoiseGradientParam.m: Gradient of the Gaussian noise model's parameters.
gaussianNoiseNuG.m: Update nu and g parameters associated with Gaussian noise model.
gaussianNoiseOut.m: Output from Gaussian noise model.
gaussianNoiseParamInit.m: Gaussian noise model's parameter initialisation.
gaussianNoiseSites.m: Site updates for Gaussian noise model.
mgaussianLikelihood.m: Likelihood of data under Variable variance Gaussian noise model.
mgaussianLogLikelihood.m: Log-likelihood of data under Variable variance Gaussian noise model.
mgaussianNoiseDisplay.m: Display  parameters from Variable variance Gaussian noise model.
mgaussianNoiseExpandParam.m: Expand Variable variance Gaussian noise model's structure from param vector.
mgaussianNoiseExtractParam.m: Extract parameters from Variable variance Gaussian noise model.
mgaussianNoiseGradVals.m: Gradient wrt mu and varsigma of log-likelihood for Variable variance Gaussian noise model.
mgaussianNoiseGradientParam.m: Gradient of the Variable variance Gaussian noise model's parameters.
mgaussianNoiseOut.m: Ouput from Variable variance Gaussian noise model.
mgaussianNoiseParamInit.m: Variable variance Gaussian noise model's parameter initialisation.
negNoiseGradientParam.m: Wrapper function for calling noise gradients.
negNoiseLogLikelihood.m: Wrapper function for calling noise likelihoods.
ngaussLikelihood.m: Likelihood of data under noiseless Gaussian noise model.
ngaussLogLikelihood.m: Log-likelihood of data under noiseless Gaussian noise model.
ngaussNoiseDisplay.m: Display  parameters from noiseless Gaussian noise model.
ngaussNoiseExpandParam.m: Expand noiseless Gaussian noise model's structure from param vector.
ngaussNoiseExtractParam.m: Extract parameters from noiseless Gaussian noise model.
ngaussNoiseGradVals.m: Gradient wrt mu and varsigma of log-likelihood for noiseless Gaussian noise model.
ngaussNoiseGradientParam.m: Gradient of the noiseless Gaussian noise model's parameters.
ngaussNoiseNuG.m: Update nu and g parameters associated with noiseless Gaussian noise model.
ngaussNoiseOut.m: Ouput from noiseless Gaussian noise model.
ngaussNoiseParamInit.m: noiseless Gaussian noise model's parameter initialisation.
ngaussNoiseSites.m: Site updates for noiseless Gaussian noise model.
noiseCreate.m: Initialise a noise structure.
noiseDisplay.m: Display the parameters of the noise model.
noiseExpandParam.m: Expand the noise model's parameters from params vector.
noiseExtractParam.m: Extract the noise model's parameters.
noiseGradVals.m: Gradient of noise model wrt mu and varsigma.
noiseGradX.m: Returns the gradient of the log-likelihood wrt x.
noiseGradientParam.m: Gradient of the noise model's parameters.
noiseLikelihood.m: Return the likelihood for each point under the noise model.
noiseLogLikelihood.m: Return the log-likelihood under the noise model.
noiseOut.m: Give the output of the noise model given the mean and variance.
noiseParamInit.m: Noise model's parameter initialisation.
noiseTest.m: Run some tests on the specified noise model.
noiseUpdateNuG.m: Update nu and g for a given noise model.
noiseUpdateSites.m: Update site parameters for a given noise model.
orderedGradX.m: Gradient wrt x of log-likelihood for Ordered categorical model.
orderedGradientParam.m: Gradient of the Ordered categorical model's parameters.
orderedLikelihood.m: Likelihood of data under ordered categorical noise model.
orderedLogLikelihood.m: Log-likelihood of data under ordered categorical noise model.
orderedNoiseDisplay.m: Display the parameters of the ordered categorical noise model.
orderedNoiseExpandParam.m: Expand ordered categorical noise model's structure from param vector.
orderedNoiseExtractParam.m: Extract parameters from ordered categorical noise model.
orderedNoiseGradVals.m: Gradient wrt mu and varsigma of log-likelihood for ordered categorical noise model.
orderedNoiseGradientParam.m: Gradient of the ordered categorical noise model's parameters.
orderedNoiseOut.m: Output from ordered categorical noise model.
orderedNoiseParamInit.m: Ordered categorical noise model's parameter initialisation.
orderedNoiseUpdateParams.m: Update parameters for ordered categorical noise model.
probitLikelihood.m: Likelihood of data under probit noise model.
probitLogLikelihood.m: Log-likelihood of data under probit noise model.
probitNoiseDisplay.m: Display the parameters of the Probit noise model.
probitNoiseExpandParam.m: Expand probit noise structure from param vector.
probitNoiseExtractParam.m: Extract parameters from probit noise model.
probitNoiseGradVals.m: Gradient wrt mu and varsigma of log-likelihood for probit noise model.
probitNoiseGradientParam.m: Gradient of the probit noise model's parameters.
probitNoiseOut.m: Output from probit noise model.
probitNoiseParamInit.m: probistic classification model's parameter initialisation.