You are an expert in writing code in many different languages. Your goal is to perform code completion for the following code, keeping in mind the rest of the code and the file meta data.
    Metadata details:
    filename: Contents.m
    path of file: ./repos/PRMLT
    
    the code of the file until where you have to start completion: % CHAPTER01
%   condEntropy      - Compute conditional entropy z=H(x|y) of two discrete variables x and y.
%   entropy          - Compute entropy z=H(x) of a discrete variable x.
%   jointEntropy     - Compute joint entropy z=H(x,y) of two discrete variables x and y.
%   mutInfo          - Compute mutual information I(x,y) of two discrete variables x and y.
%   nmi              - Compute normalized mutual information I(x,y)/sqrt(H(x)*H(y)) of two discrete variables x and y.
%   nvi              - Compute normalized variation information z=(1-I(x,y)/H(x,y)) of two discrete variables x and y.
%   relatEntropy     - Compute relative entropy (a.k.a KL divergence) z=KL(p(x)||p(y)) of two discrete variables x and y.
% CHAPTER02    
%   logDirichlet     - Compute log pdf of a Dirichlet distribution.
%   logGauss         - Compute log pdf of a Gaussian distribution.
%   logKde           - Compute log pdf of kernel density estimator.
%   logMn            - Compute log pdf of a multinomial distribution.
%   logMvGamma       - Compute logarithm multivariate Gamma function 
%   logSt            - Compute log pdf of a Student's t distribution.
%   logVmf           - Compute log pdf of a von Mises-Fisher distribution.
%   logWishart       - Compute log pdf of a Wishart distribution.
% CHAPTER03    
%   linReg           - Fit linear regression model y=w'x+w0  
%   linRegFp         - Fit empirical Bayesian linear model with Mackay fixed point method (p.168)
%   linRegPred       - Compute linear regression model reponse y = w'*X+w0 and likelihood
%   linRnd           - Generate data from a linear model p(t|w,x)=G(w'x+w0,sigma), sigma=sqrt(1/beta) 
% CHAPTER04    
%   binPlot          - Plot binary classification result for 2d data
%   fda              - Fisher (linear) discriminant analysis
%   logitBin         - Logistic regression for binary classification optimized by Newton-Raphson method.
%   logitBinPred     - Prediction of binary logistic regression model
%   logitMn          - Multinomial regression for multicla
    
function 
%   logSt            - Compute log pdf of a Student's t distribution.
%   logVmf           - Compute log pdf of a von Mises-Fisher distribution.
%   logWishart       - Compute log pdf of a Wishart distribution.
% CHAPTER03    
%   linReg           - Fit linear regression model y=w'x+w0  
%   linRegFp         - Fit empirical Bayesian linear model with Mackay fixed point method (p.168)
%   linRegPred       - Compute linear regression model reponse y = w'*X+w0 and likelihood
%   linRnd           - Generate data from a linear model p(t|w,x)=G(w'x+w0,sigma), sigma=sqrt(1/beta) 
% CHAPTER04    
%   binPlot          - Plot binary classification result for 2d data
%   fda              - Fisher (linear) discriminant analysis
%   logitBin         - Logistic regression for binary classification optimized by Newton-Raphson method.
%   logitBinPred     - Prediction of binary logistic regression model
%   logitMn          - Multinomial regression for multiclass problem (Multinomial likelihood)
%   logitMnPred      - Prediction of multiclass (multinomial) logistic regression model
%   sigmoid          - Sigmod function
%   softmax          - Softmax function
% CHAPTER05
%   mlpClass         - Train a multilayer perceptron neural network for classification with backpropagation
%   mlpClassPred     - Multilayer perceptron classification prediction
%   mlpReg           - Train a multilayer perceptron neural network for regression with backpropagation
%   mlpRegPred       - Multilayer perceptron regression prediction
% CHAPTER06    
%   kn2sd            - Transform a kernel matrix (or inner product matrix) to a squared distance matrix
%   knCenter         - Centerize the data in the kernel space
%   knGauss          - Gaussian (RBF) kernel K = exp(-|x-y|/(2s));
%   knKmeans         - Perform kernel kmeans clustering.
%   knKmeansPred     - Prediction for kernel kmeans clusterng
%   knLin            - Linear kernel (inner product)
%   knPca            - Kernel PCA
%   knPcaPred        - Prediction for kernel PCA
%   knPoly           - Polynomial kernel k(x,y)=(x'y+c)^o
%   knReg            - Gaussian process (kernel) regression
%   knRegPred        - Prediction for Gaussian Process (kernel) regression model
%   sd2kn            - Transform a squared distance matrix to a kernel matrix. 
% CHAPTER07    
%   rvmBinFp         - Relevance Vector Machine (ARD sparse prior) for binary classification.
%   rvmBinPred       - Prodict the label for binary logistic regression model
%   rvmRegFp         - Relevance Vector Machine (ARD sparse prior) for regression
%   rvmRegPred       - Compute RVM regression model reponse y = w'*X+w0 and likelihood 
%   rvmRegSeq        - Sparse Bayesian Regression (RVM) using sequential algorithm
% CHAPTER08    
%  MRF    
%   mrfBethe         - Compute Bethe energy
%   mrfBp            - Undirected graph belief propagation for MRF
%   mrfGibbs         - Compute Gibbs energy
%   mrfIsGa          - Contruct a latent Ising MRF with Gaussian observation
%   mrfMf            - Mean field for MRF
%  NaiveBayes    
%   nbBern           - Naive bayes classifier with indepenet Bernoulli.
%   nbBernPred       - Prediction of naive Bayes classifier with independ