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: infExact_fastrobust.m
    path of file: ./repos/bads/gpml_fast
    
    the code of the file until where you have to start completion: function [post,nlZ,dnlZ] = infExact_fastrobust(kmax, hyp, mean, cov, lik, x, y, s)

% Fast, robust inference for a GP with Gaussian likelihood. Compute a 
% parametrization of the posterior, the negative log marginal likelihood 
% and its derivatives w.r.t. the hyperparameters. See also "help infMethods".
% 
% To be used in combination with 'fast' covariance functions (e.g.
% covSEard_fast and such). 
%
% This implementation also handles badly conditioned covariance matrices, 
% which sometimes become non-positive definite and cause a failure of the 
% Cholesky decomposition. In these cases, the covariance matrix is corrected 
% to the nearest symmetric positive semidefinite (SPD) matrix in Frobenius 
% norm (see [1]; based on an implementation by John d'Errico).
%
% [1] Higham NJ, "Computing a nearest symmetric positive semidefinite 
% matrix", Linear Algebra Appl, 1988.
% http://www.sciencedirect.com/science/article/pii/0024379588902236
%
% Added support for 'fast' inference and robust Cholesky decomposition by
% Luigi Acerbi, 2016-01-03.
% Original code by Carl Edward Rasmussen and Hannes Nickisch, 2015-07-13.

    
function [post,nlZ,dnlZ] = infExact_fastrobust(kmax, hyp, mean, cov, lik, x, y, s)

% Fast, robust inference for a GP with Gaussian likelihood. Compute a 
% parametrization of the posterior, the negative log marginal likelihood 
% and its derivatives w.r.t. the hyperparameters. See also "help infMethods".
% 
% To be used in combination with 'fast' covariance functions (e.g.
% covSEard_fast and such). 
%
% This implementation also handles badly conditioned covariance matrices, 
% which sometimes become non-positive definite and cause a failure of the 
% Cholesky decomposition. In these cases, the covariance matrix is corrected 
% to the nearest symmetric positive semidefinite (SPD) matrix in Frobenius 
% norm (see [1]; based on an implementation by John d'Errico).
%
% [1] Higham NJ, "Computing a nearest symmetric positive semidefinite 
% matrix", Linear Algebra Appl, 1988.
% http://www.sciencedirect.com/science/article/pii/0024379588902236
%
% Added support for 'fast' inference and robust Cholesky decomposition by
% Luigi Acerbi, 2016-01-03.
% Original code by Carl Edward Rasmussen and Hannes Nickisch, 2015-07-13.
%
% See also INFMETHODS.M.

if iscell(lik), likstr = lik{1}; else likstr = lik; end