%% Create and Adjust Default Sampler Options
% Suppose that you plan to estimate, simulate, or forecast a Bayesian linear regression model that has a custom joint prior distribution. In this case, MATLAB® resorts to MCMC sampling for posterior simulation and estimation. You can choose a sampler and tune its parameters using a sampler options structure.
% Create a default sampler options structure.

options = sampleroptions

% options specifies the slice sampler, and its typical width is empty. An empty width indicates usage of the default width for posterior sampling.
%
% Specify a typical width of 10 for the slice sampler.

options.Width = 10

% To implement slice sampling with a sample width of 10 for posterior estimation, create a customblm model, and then specify sampler options structure options by using the 'Options' name-value pair argument of estimate, simulate, or forecast.
% To specify a different MCMC sampler, create a new sampler options structure.
% Copyright 2012 The MathWorks, Inc.