L = cmpndLikelihood(noise, mu, varsigma, y)

 L = cmpndLogLikelihood(noise, mu, varsigma, y)

 cmpndNoiseDisplay(noise)

 noise = cmpndNoiseExpandParam(noise, params)

 params = cmpndNoiseExtractParam(noise)

 [dlnZ_dmu, dlnZ_dvs] = cmpndNoiseGradVals(noise, mu, varsigma, y)

 g = cmpndNoiseGradientParam(noise, mu, varsigma, y)

 [g, nu] = cmpndNoiseNuG(noise, mu, varSigma, y)

 y = cmpndNoiseOut(noise, mu, varsigma)

 noise = cmpndNoiseParamInit(noise, y)

 [m, beta] = cmpndNoiseSites(noise, g, nu, mu, varSigma, y)

 L = gaussianLikelihood(noise, mu, varsigma, y)

 L = gaussianLogLikelihood(noise, mu, varsigma, y)

 h = gaussianNoise3dPlot(noise, plotType, CX, CY, CZ, CZVar, varargin)

 gaussianNoiseDisplay(noise)

 noise = gaussianNoiseExpandParam(noise, params)

 [params, names] = gaussianNoiseExtractParam(noise)

 [dlnZ_dmu, dlnZ_dvs] = gaussianNoiseGradVals(noise, mu, varsigma, y)

 g = gaussianNoiseGradientParam(noise, mu, varsigma, y)

 [g, nu] = gaussianNoiseNuG(noise, mu, varSigma, y)

 y = gaussianNoiseOut(noise, mu, varsigma)

 noise = gaussianNoiseParamInit(noise, y)

 h = gaussianNoisePointPlot(noise, X, y, ...
%                               fontName, fontSize, ...
%                               markerSize, lineWidth);

 [m, beta] = gaussianNoiseSites(noise, g, nu, mu, varSigma, y)

 L = mgaussianLikelihood(noise, mu, varsigma, y)

 L = mgaussianLogLikelihood(noise, mu, varsigma, y)

 mgaussianNoiseDisplay(noise)

 noise = mgaussianNoiseExpandParam(noise, params)

 [params, names] = mgaussianNoiseExtractParam(noise)

 [dlnZ_dmu, dlnZ_dvs] = mgaussianNoiseGradVals(noise, mu, varsigma, y)

 g = mgaussianNoiseGradientParam(noise, mu, varsigma, y)

 y = mgaussianNoiseOut(noise, mu, varsigma)

 noise = mgaussianNoiseParamInit(noise, y)

 g = negNoiseGradientParam(params, model, prior)

 e = negNoiseLogLikelihood(params, model, prior)

 L = ngaussLikelihood(noise, mu, varsigma, y)

 L = ngaussLogLikelihood(noise, mu, varsigma, y)

 ngaussNoiseDisplay(noise)

 noise = ngaussNoiseExpandParam(noise, params)

 [params, names] = ngaussNoiseExtractParam(noise)

 [dlnZ_dmu, dlnZ_dvs] = ngaussNoiseGradVals(noise, mu, varsigma, y)

 g = ngaussNoiseGradientParam(noise, mu, varsigma, y)

 [g, nu] = ngaussNoiseNuG(noise, mu, varSigma, y)

 y = ngaussNoiseOut(noise, mu, varsigma)

 noise = ngaussNoiseParamInit(noise, y)

 [m, beta] = ngaussNoiseSites(noise, g, nu, mu, varSigma, y)

 h = noise3dPlot(noise, plotType, CX, CY, CZ, CZVar, varargin)

 noise = noiseCreate(noiseType, y)

 noiseDisplay(noise)

 noise = noiseExpandParam(noise, params)

 [params, names] = noiseExtractParam(noise)

 [dlnZ_dmu, dlnZ_dvs] = noiseGradVals(noise, mu, varsigma, y)

 g = noiseGradX(noise, mu, varsigma, dmu, dvs, y);

 g = noiseGradientParam(noise, mu, varsigma, y)

 L = noiseLikelihood(noise, mu, varsigma, y);

 L = noiseLogLikelihood(noise, mu, varsigma, y);

 y = noiseOut(noise, mu, varsigma);

 noise = noiseParamInit(noise, y)

 h = noisePointPlot(noise, X, y,  ...
%                         fontName, fontSize, ...
%                         markerSize, lineWidth)

 noise = noiseReadFromFID(FID)

 noise = noiseReadParamsFromFID(type, FID)

 noise = noiseTest(noiseType);

 [g, nu] = noiseUpdateNuG(noise, mu, varSigma, y);

 [m, beta] = noiseUpdateSites(noise, g, nu, mu, varSigma, y);

 g = orderedGradX(X, Y, model, prior)

 g = orderedGradientParam(model, params)

 L = orderedLikelihood(noise, mu, varsigma, y)

 L = orderedLogLikelihood(noise, mu, varsigma, y)

 h = orderedNoise3dPlot(noise, plotType, CX, CY, CZ, CZVar, varargin)

 orderedNoiseDisplay(noise)

 noise = orderedNoiseExpandParam(noise, params)

 [params, names] = orderedNoiseExtractParam(noise)

 [dlnZ_dmu, dlnZ_dvs] = orderedNoiseGradVals(noise, mu, varsigma, y)

 g = orderedNoiseGradientParam(noise, mu, varsigma, y)

 y = orderedNoiseOut(noise, mu, varsigma)

 noise = orderedNoiseParamInit(noise, y)

 h = orderedNoisePointPlot(noise, X, y, ...
%                               fontName, fontSize, ...
%                               markerSize, lineWidth);

 [nu, g] = orderedNoiseUpdateParams(noise, mu, varsigma, y, index)

 L = probitLikelihood(noise, mu, varsigma, y)

 L = probitLogLikelihood(noise, mu, varsigma, y)

 h = probitNoise3dPlot(noise, plotType, CX, CY, CZ, CZVar, varargin)

 probitNoiseDisplay(noise)

 noise = probitNoiseExpandParam(noise, params)

 [params, names] = probitNoiseExtractParam(noise)

 [dlnZ_dmu, dlnZ_dvs] = probitNoiseGradVals(noise, mu, varsigma, y)

 g = probitNoiseGradientParam(noise, mu, varsigma, y)

 y = probitNoiseOut(noise, mu, varsigma)

 noise = probitNoiseParamInit(noise, y)

 h = probitNoisePointPlot(noise, X, y, ...
%                               fontName, fontSize, ...
%                               markerSize, lineWidth);

 scaleNoiseDisplay(noise)

 noise = scaleNoiseExpandParam(noise, params)

 y = scaleNoiseOut(noise, mu, varSigma)

 noise = scaleNoiseParamInit(noise, y)

 [m, beta] = scaleNoiseSites(noise, g, nu, mu, varSigma, y)