66 lines
2.4 KiB
C++
66 lines
2.4 KiB
C++
/*
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* Copyright (C) 2009-2013 Dynare Team
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*
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* This file is part of Dynare.
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*
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* Dynare is free software: you can redistribute it and/or modify
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* it under the terms of the GNU General Public License as published by
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* the Free Software Foundation, either version 3 of the License, or
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* (at your option) any later version.
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*
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* Dynare is distributed in the hope that it will be useful,
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* but WITHOUT ANY WARRANTY; without even the implied warranty of
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* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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* GNU General Public License for more details.
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*
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* You should have received a copy of the GNU General Public License
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* along with Dynare. If not, see <http://www.gnu.org/licenses/>.
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*/
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///////////////////////////////////////////////////////////
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// LogPosteriorDensity.hh
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// Implementation of the Class LogPosteriorDensity
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// Created on: 10-Feb-2010 20:54:18
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///////////////////////////////////////////////////////////
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#if !defined(LPD_052A31B5_53BF_4904_AD80_863B52827973__INCLUDED_)
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#define LPD_052A31B5_53BF_4904_AD80_863B52827973__INCLUDED_
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#include "EstimatedParametersDescription.hh"
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#include "LogPriorDensity.hh"
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#include "LogLikelihoodMain.hh"
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/**
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* Class that calculates Log Posterior Density using kalman, based on Dynare
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* DsgeLikelihood.m
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*/
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class LogPosteriorDensity
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{
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private:
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LogPriorDensity logPriorDensity;
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LogLikelihoodMain logLikelihoodMain;
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public:
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virtual ~LogPosteriorDensity();
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LogPosteriorDensity(const std::string &modName, EstimatedParametersDescription &estParamsDesc, size_t n_endo, size_t n_exo,
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const std::vector<size_t> &zeta_fwrd_arg, const std::vector<size_t> &zeta_back_arg, const std::vector<size_t> &zeta_mixed_arg,
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const std::vector<size_t> &zeta_static_arg, const double qz_criterium_arg, const std::vector<size_t> &varobs_arg,
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double riccati_tol_arg, double lyapunov_tol_arg,
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bool noconstant_arg);
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template <class VEC1, class VEC2>
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double
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compute(VEC1 &steadyState, VEC2 &estParams, VectorView &deepParams, const MatrixConstView &data, MatrixView &Q, Matrix &H, size_t presampleStart)
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{
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return -logLikelihoodMain.compute(steadyState, estParams, deepParams, data, Q, H, presampleStart)
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-logPriorDensity.compute(estParams);
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}
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Vector&getLikVector();
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};
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#endif // !defined(052A31B5_53BF_4904_AD80_863B52827973__INCLUDED_)
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