111 lines
3.8 KiB
Matlab
111 lines
3.8 KiB
Matlab
|
function Scale = calibrate_mh_scale_parameter(ObjectiveFunction, CovarianceMatrix, Parameters, MhBounds, options, varargin)
|
||
|
|
||
|
% Tune the MH scale parameter so that the overall acceptance ratio is close to AcceptanceTarget.
|
||
|
%
|
||
|
% INPUTS
|
||
|
% - ObjectiveFunction [fhandle] Function (posterior kernel).
|
||
|
% - CovarianceMatrix [double] n*n matrix, covariance matrix of the jumping distribution.
|
||
|
% - Parameters [double] n*1 vector, parameter values.
|
||
|
% - MhBounds [double] n*2 matrix, bounds on the possible values for the parameters.
|
||
|
% - options [structure] content of options_.tune_mh_jscale.
|
||
|
% - varargin [cell] Additional arguments to be passed to ObjectiveFunction.
|
||
|
%
|
||
|
% OUTPUTS
|
||
|
% - Scale [double] scalar, optimal scale parameter for teh jumping distribution.
|
||
|
|
||
|
% Fire up the wait bar
|
||
|
hh = dyn_waitbar(0,'Tuning of the scale parameter...');
|
||
|
set(hh,'Name','Tuning of the scale parameter.');
|
||
|
|
||
|
% Intilialize various counters.
|
||
|
j = 1; jj = 1; isux = 0; jsux = 0; i = 0;
|
||
|
|
||
|
% Evaluate the objective function.
|
||
|
logpo0 = - feval(ObjectiveFunction, Parameters, varargin{:});
|
||
|
logpo1 = logpo0;
|
||
|
|
||
|
% Get the dimension of the problem.
|
||
|
n = length(Parameters);
|
||
|
|
||
|
% Initialize the correction on the scale factor.
|
||
|
correction = 1.0;
|
||
|
|
||
|
% Set the initial value of the scale parameter
|
||
|
Scale = options.guess;
|
||
|
|
||
|
% Transposition of some arrays.
|
||
|
MhBounds = MhBounds';
|
||
|
Parameters = Parameters';
|
||
|
|
||
|
% Compute the Cholesky of the covariance matrix, return an error if the
|
||
|
% matrix is not positive definite.
|
||
|
try
|
||
|
dd = chol(CovarianceMatrix);
|
||
|
catch
|
||
|
error('The covariance matrix has to be a symetric positive definite matrix!')
|
||
|
end
|
||
|
|
||
|
% Set parameters related to the proposal distribution
|
||
|
if options.rwmh.proposal_distribution=='rand_multivariate_normal'
|
||
|
nu = n;
|
||
|
elseif options.rwmh.proposal_distribution=='rand_multivariate_student'
|
||
|
nu = options.rwmh.student_degrees_of_freedom;
|
||
|
end
|
||
|
|
||
|
% Random Walk Metropolis Hastings iterations...
|
||
|
while j<=options.maxiter
|
||
|
% Obtain a proposal (jump)
|
||
|
proposal = feval(options.rwmh.proposal_distribution, Parameters, Scale*dd, nu);
|
||
|
% If out of boundaries set the posterior kernel equal to minus infinity
|
||
|
% so that the proposal will be rejected with probability one.
|
||
|
if all(proposal > MhBounds(1,:)) && all(proposal < MhBounds(2,:))
|
||
|
logpo0 = -feval(ObjectiveFunction, proposal(:), varargin{:});
|
||
|
else
|
||
|
logpo0 = -inf;
|
||
|
end
|
||
|
% Move if the proposal is enough likely...
|
||
|
if logpo0>-inf && log(rand)<logpo0-logpo1
|
||
|
Parameters = proposal;
|
||
|
logpo1 = logpo0;
|
||
|
isux = isux + 1;
|
||
|
jsux = jsux + 1;
|
||
|
end% ... otherwise I don't move.
|
||
|
prtfrc = j/options.maxiter;
|
||
|
% Update the waitbar
|
||
|
if ~mod(j, 10)
|
||
|
dyn_waitbar(prtfrc, hh, sprintf('Acceptance ratio [during last 500]: %f [%f]', isux/j, jsux/jj));
|
||
|
end
|
||
|
% Adjust the value of the scale parameter.
|
||
|
if ~mod(j, options.stepsize)
|
||
|
r1 = jsux/jj; % Local acceptance ratio
|
||
|
r2 = isux/j; % Overall acceptance ratio
|
||
|
% Set correction for the scale factor
|
||
|
c1 = r1/options.target;
|
||
|
if abs(c1-1)>.05
|
||
|
correction = correction^options.rho*c1^(1-options.rho);
|
||
|
else
|
||
|
correction = c1;
|
||
|
end
|
||
|
% Apply correction
|
||
|
if c1>0
|
||
|
Scale = Scale*correction;
|
||
|
else
|
||
|
Scale = Scale/10;
|
||
|
end
|
||
|
% Update some counters.
|
||
|
jsux = 0; jj = 0;
|
||
|
if abs(r2-options.target)<options.c2 && abs(r1-options.target)<options.c1
|
||
|
i = i+1;
|
||
|
else
|
||
|
i = 0;
|
||
|
end
|
||
|
% Test convergence.
|
||
|
if i>options.c3
|
||
|
break
|
||
|
end
|
||
|
end
|
||
|
j = j+1;
|
||
|
jj = jj + 1;
|
||
|
end
|
||
|
|
||
|
dyn_waitbar_close(hh);
|