SCGoptim                Optimise the given function using (scaled)
                        conjugate gradients.
basePlot                Plot a contour of the 2D Gaussian distribution
                        with covariance matrix K.
cmpndKernParamInit      CMPND kernel parameter initialisation.
cmpndNoiseParamInit     CMPND noise parameter initialisation.
demAutoOptimiseGp       Gaussian Process Optimisation Demo
demGpCov2D              Gaussian Process 2D Covariance Demo
demGpSample             Gaussian Process Sampling Demo
demInterpolation        Gaussian Process Interpolation Demo
demOptimiseGp           Gaussian Process Optimisation Demo
demRegression           Gaussian Process Regression Demo
expTransform            Constrains a parameter.
gaussSamp               Sample from a Gaussian with a given covariance.
gaussianNoiseOut        Compute the output of the GAUSSIAN noise given
                        the input mean and variance.
gaussianNoiseParamInit
                        GAUSSIAN noise parameter initialisation.
gpBlockIndices          Return indices of given block.
gpComputeAlpha          Update the vector 'alpha' for computing
                        posterior mean quickly.
gpComputeM              Compute the matrix m given the model.
gpCovGrads              Sparse objective function gradients wrt
                        Covariance functions for inducing variables.
gpCovGradsTest          Test the gradients of the likelihood wrt the
                        covariance.
gpCreate                Create a GP model with inducing
                        variables/pseudo-inputs.
gpDataIndices           Return indices of present data.
gpExpandParam           Expand a parameter vector into a GP model.
gpExtractParam          Extract a parameter vector from a GP model.
gpGradient              Gradient wrapper for a GP model.
gpLogLikeGradients      Compute the gradients for the parameters and X.
gpLogLikelihood         Compute the log likelihood of a GP.
gpMeanFunctionGradient
                        Compute the log likelihood gradient wrt the
                        scales.
gpObjective             Wrapper function for GP objective.
gpOptimise              Optimise the inducing variable based kernel.
gpOptions               Return default options for GP model.
gpOut                   Evaluate the output of an Gaussian process
                        model.
gpPlot                  Gaussian Process Plotter
gpPosteriorMeanVar      Mean and variances of the posterior at points
                        given by X.
gpPosteriorSample       Plot Samples from a GP Posterior.
gpSample                Plot Samples from a GP.
gpScaleBiasGradient     Compute the log likelihood gradient wrt the
                        scales.
gpTest                  Test the gradients of the gpCovGrads function
                        and the gp models.
gpUpdateAD              Update the representations of A and D
                        associated with the model.
gpUpdateKernels         Update the kernels that are needed.
kernCompute             Compute the kernel given the parameters and X.
kernCreate              Initialise a kernel structure.
kernDiagGradX           Compute the gradient of the kernel wrt X.
kernDiagGradient        Compute the gradient of the kernel's parameters
                        for the diagonal.
kernGradient            Compute the gradient wrt the kernel parameters.
kernParamInit           Kernel parameter initialisation.
kernTest                Run some tests on the specified kernel.
modelDisplay            Display a model.
modelExpandParam        Update a model structure with new parameters or
                        update the posterior processes.
modelExtractParam       Extract the parameters of a model.
modelGradient           Model log-likelihood/objective error function
                        and its gradient.
modelGradientCheck      Check gradients of given model.
modelOut                Give the output of a model for given X.
modelOutputGrad         Compute derivatives with respect to params of
                        model outputs.
multiKernParamInit      MULTI kernel parameter initialisation.
noiseCreate             Initialise a noise structure.
noiseOut                Give the output of the noise model given the
                        mean and variance.
noiseParamInit          Noise model's parameter initialisation.
optimiDefaultConstraint
                        Returns function for parameter constraint.
rbfKernDiagGradX        Gradient of RBF kernel's diagonal with respect
                        to X.
rbfKernGradX            Gradient of RBF kernel with respect to input
                        locations.
rbfKernParamInit        RBF kernel parameter initialisation.
whiteKernDiagGradX      Gradient of WHITE kernel's diagonal with
                        respect to X.
whiteKernGradX          Gradient of WHITE kernel with respect to input
                        locations.
whiteKernParamInit      WHITE kernel parameter initialisation.
zeroAxes                A function to move the axes crossing point to
                        the origin.
