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