function [LIK, lik] = gaussian_mixture_filter(ReducedForm, Y, start, ParticleOptions, ThreadsOptions, options_, M_)

% Evaluates the likelihood of a non-linear model approximating the state
% variables distributions with Gaussian mixtures.
%
% INPUTS:
% - ReducedForm            [struct]    MATLAB's structure describing the reduced form model.
%                                         reduced_form_model.measurement.H   [double]   (pp x pp) variance matrix of measurement errors.
%                                         reduced_form_model.state.Q         [double]   (qq x qq) variance matrix of state errors.
%                                         reduced_form_model.state.dr        [structure] output of resol.m.
% - Y                      [double]    pp×smpl matrix of (detrended) data, where pp is the maximum number of observed variables.
% - start                  [integer]   scalar, likelihood evaluation starts at 'start'.
% - ParticleOptions        [struct]    filter options
% - ThreadsOptions         [struct]    options for threading of mex files
% - options_               [struct]    describing the options
% - M_                     [struct]    describing the model
%
% OUTPUTS:
% - LIK                    [double]    scalar, likelihood
% - lik                    [double]    vector, density of observations in each period.
%
% Remarks:
% Gaussian Mixture allows reproducing a wide variety of generalized distributions (when multimodal for instance).
% Each Gaussian distribution is obtained whether
%   - with a radial-spherical cubature
%   - with scaled unscented sigma-points
% A Sparse grid Kalman Filter is implemented on each component of the mixture,
% which confers it a weight about current information.
% Information on the current observables is then embodied in the proposal
% distribution in which we draw particles, which allows
%   - reaching a greater precision relatively to a standard particle filter,
%   - reducing the number of particles needed,
%   - still being faster.
%
% REFERENCES
%
% Van der Meerwe & Wan, Gaussian Mixture Sigma-Point Particle Filters for Sequential
% Probabilistic Inference in Dynamic State-Space Models.
% Heiss & Winschel, 2010, Journal of Applied Economics.
% Winschel & Kratzig, 2010, Econometrica.
%
% NOTES
%   The vector "lik" is used to evaluate the Jacobian of the likelihood.

% Copyright © 2009-2026 Dynare Team
%
% This file is part of Dynare.
%
% Dynare is free software: you can redistribute it and/or modify
% it under the terms of the GNU General Public License as published by
% the Free Software Foundation, either version 3 of the License, or
% (at your option) any later version.
%
% Dynare is distributed in the hope that it will be useful,
% but WITHOUT ANY WARRANTY; without even the implied warranty of
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
% GNU General Public License for more details.
%
% You should have received a copy of the GNU General Public License
% along with Dynare.  If not, see <https://www.gnu.org/licenses/>.

if ParticleOptions.pruning
    error('gaussian_mixture_filter: pruning is not yet implemented.')
end
% Set default
if isempty(start)
    start = 1;
end

mf0 = ReducedForm.mf0;
mf1 = ReducedForm.mf1;
sample_size = size(Y,2);
number_of_state_variables = length(mf0);
number_of_observed_variables = length(mf1);
number_of_structural_innovations = length(ReducedForm.Q);
G = ParticleOptions.mixture_state_variables;           % number of GM components in state
number_of_particles = ParticleOptions.number_of_particles;

% compute gaussian quadrature nodes and weights on states and shocks
if ParticleOptions.distribution_approximation.cubature
    [nodes, weights] = spherical_radial_sigma_points(number_of_state_variables);
elseif ParticleOptions.distribution_approximation.unscented
    [nodes, weights] = unscented_sigma_points(number_of_state_variables, ParticleOptions.unscented);
else
    if ParticleOptions.distribution_approximation.montecarlo
        error('The montecarlo approximation for the distribution is not implemented.')
    else
        error('This approximation for the distribution is unknown.')
    end
end

% Get covariance matrices
Q = ReducedForm.Q;
H = ReducedForm.H;
if isempty(H)
    H = 0;
    H_lower_triangular_cholesky = 0;
else
    H_lower_triangular_cholesky = reduced_rank_cholesky(H)';
end
Q_lower_triangular_cholesky = reduced_rank_cholesky(Q)';

% Initialize mixtures
StateWeights = ones(1, G)/G;
StateMu = ReducedForm.StateVectorMean;
StateSqrtP = zeros(number_of_state_variables, number_of_state_variables, G);
temp = reduced_rank_cholesky(ReducedForm.StateVectorVariance)';
StateMu = bsxfun(@plus, StateMu, bsxfun(@times,diag(temp), (-(G-1)/2:1:(G-1)/2))/10);
for g=1:G
    StateSqrtP(:,:,g) = temp/sqrt(G) ;
end

if ~ParticleOptions.mixture_structural_shocks
    StructuralShocksMu = zeros(1, number_of_structural_innovations);
    StructuralShocksWeights = 1;
    I = 1;
    StructuralShocksMu = Q_lower_triangular_cholesky*StructuralShocksMu';
    StructuralShocksSqrtP = zeros(number_of_structural_innovations, number_of_structural_innovations, I);
    StructuralShocksSqrtP(:,:,1) = Q_lower_triangular_cholesky;
elseif ParticleOptions.mixture_structural_shocks==1
    if ParticleOptions.proposal_approximation.cubature
        [StructuralShocksMu, StructuralShocksWeights] = spherical_radial_sigma_points(number_of_structural_innovations);
        StructuralShocksWeights = ones(size(StructuralShocksMu, 1), 1)*StructuralShocksWeights;
    elseif ParticleOptions.proposal_approximation.unscented
        [StructuralShocksMu, StructuralShocksWeights] = unscented_sigma_points(number_of_structural_innovations, ParticleOptions.unscented);
    else
        if ParticleOptions.proposal_approximation.montecarlo
            error('The montecarlo approximation for the proposal is not implemented.')
        else
            error('This approximation for the proposal is unknown.')
        end
    end
    I = size(StructuralShocksWeights, 1);
    StructuralShocksMu = Q_lower_triangular_cholesky*StructuralShocksMu';
    StructuralShocksSqrtP = zeros(number_of_structural_innovations, number_of_structural_innovations, I);
    for i=1:I
        StructuralShocksSqrtP(:,:,i) = Q_lower_triangular_cholesky;
    end
else
    if ParticleOptions.proposal_approximation.cubature
        [StructuralShocksMu, StructuralShocksWeights] = spherical_radial_sigma_points(number_of_structural_innovations);
        StructuralShocksWeights = ones(size(StructuralShocksMu, 1), 1)*StructuralShocksWeights ;
    elseif ParticleOptions.proposal_approximation.unscented
        [StructuralShocksMu, StructuralShocksWeights] = unscented_sigma_points(number_of_structural_innovations, ParticleOptions.unscented);
    else
        if ParticleOptions.proposal_approximation.montecarlo
            error('The montecarlo approximation for the proposal is not implemented.')
        else
            error('This approximation for the proposal is unknown.')
        end
    end
    I = size(StructuralShocksWeights, 1);
    StructuralShocksMu = Q_lower_triangular_cholesky*StructuralShocksMu';
    StructuralShocksSqrtP = zeros(number_of_structural_innovations, number_of_structural_innovations, I);
    for i=1:I
        StructuralShocksSqrtP(:,:,i) = Q_lower_triangular_cholesky/sqrt(StructuralShocksWeights(i));
    end
end

ObservationShocksWeights = 1;
J = 1 ;

Gprime = G*I;
Gsecond = G*I*J;
SampleWeights = ones(Gsecond, 1)/Gsecond;

StateWeightsPrior = zeros(1,Gprime);
StateMuPrior = zeros(number_of_state_variables,Gprime);
StateSqrtPPrior = zeros(number_of_state_variables, number_of_state_variables, Gprime);

StateWeightsPost = zeros(1, Gsecond);
StateMuPost = zeros(number_of_state_variables, Gsecond);
StateSqrtPPost = zeros(number_of_state_variables, number_of_state_variables, Gsecond);

const_lik = (2*pi)^(.5*number_of_observed_variables);

lik  = NaN(sample_size, 1);
LIK  = NaN;
for t=1:sample_size
    % Build the proposal joint quadratures of Gaussian on states, structural
    % shocks and observation shocks based on each combination of mixtures
    for i=1:I
        for j=1:J
            for g=1:G
                gprime = g + (i-1)*G;
                gsecond = gprime + (j-1)*Gprime;
                [StateMuPrior(:,gprime), StateSqrtPPrior(:,:,gprime), StateWeightsPrior(1,gprime), ...
                 StateMuPost(:,gsecond), StateSqrtPPost(:,:,gsecond), StateWeightsPost(1,gsecond)] = ...
                    gaussian_mixture_filter_bank(ReducedForm,Y(:,t), StateMu(:,g), StateSqrtP(:,:,g), StateWeights(g),...
                                                 StructuralShocksMu(:,i), StructuralShocksSqrtP(:,:,i), StructuralShocksWeights(i),...
                                                 ObservationShocksWeights(j), H, H_lower_triangular_cholesky, const_lik, ...
                                                 ParticleOptions, options_, M_);
            end
        end
    end

    % Normalize weights
    StateWeightsPrior = StateWeightsPrior/sum(StateWeightsPrior, 2);
    StateWeightsPost = StateWeightsPost/sum(StateWeightsPost, 2);

    if ParticleOptions.distribution_approximation.cubature || ParticleOptions.distribution_approximation.unscented
        for i=1:Gsecond
            StateParticles = bsxfun(@plus, StateMuPost(:,i), StateSqrtPPost(:,:,i)*nodes');
            IncrementalWeights = gaussian_mixture_densities(Y(:,t), StateMuPrior, StateSqrtPPrior, StateWeightsPrior, ...
                                                            StateMuPost, StateSqrtPPost, StateWeightsPost, StateParticles, H, ...
                                                            ReducedForm, options_, M_);
            SampleWeights(i) = sum(StateWeightsPost(i)*weights.*IncrementalWeights);
        end
        SumSampleWeights = sum(SampleWeights);
        lik(t) = log(SumSampleWeights);
        SampleWeights = SampleWeights./SumSampleWeights;
        [~, SortedRandomIndx] = sort(rand(1,Gsecond));
        SortedRandomIndx = SortedRandomIndx(1:G);
        indx = resample(0,SampleWeights,ParticleOptions);
        indx = indx(SortedRandomIndx);
        StateMu = StateMuPost(:,indx);
        StateSqrtP = StateSqrtPPost(:,:,indx);
        StateWeights = ones(1,G)/G;
    else
        % Sample particle in the proposal distribution, ie the posterior state GM
        StateParticles = importance_sampling(StateMuPost,StateSqrtPPost,StateWeightsPost',number_of_particles);
        IncrementalWeights = gaussian_mixture_densities(Y(:,t), StateMuPrior, StateSqrtPPrior, StateWeightsPrior, ...
                                                        StateMuPost, StateSqrtPPost, StateWeightsPost, StateParticles, H, ...
                                                        ReducedForm, options_, M_);
        SampleWeights = IncrementalWeights/number_of_particles;
        SumSampleWeights = sum(SampleWeights,1);
        SampleWeights = SampleWeights./SumSampleWeights;
        lik(t) = log(SumSampleWeights);
        if (ParticleOptions.resampling.status.generic && neff(SampleWeights)<ParticleOptions.resampling.threshold*sample_size) || ParticleOptions.resampling.status.systematic
            StateParticles = resample(StateParticles',SampleWeights',ParticleOptions)';
            SampleWeights = ones(number_of_particles,1)/number_of_particles;
        end
        [StateMu, StateSqrtP, StateWeights] = fit_gaussian_mixture(StateParticles, SampleWeights', StateMu, StateSqrtP, StateWeights, 0.001, 10, 1);
    end
end

LIK = -sum(lik(start:end));


function [StateMu,StateSqrtP,StateWeights] = fit_gaussian_mixture(X,X_weights,StateMu,StateSqrtP,StateWeights,crit,niters,check)

[~,Ndata] = size(X);
M = size(StateMu,2) ;
if check                        % Ensure that covariances don't collapse
    MIN_COVAR_SQRT = sqrt(eps);
    init_covars = StateSqrtP;
end
eold = -Inf;
for n=1:niters
    % Calculate posteriors based on old parameters
    [~,~,marginal,posterior] = probability3(StateMu,StateSqrtP,StateWeights,X,X_weights);
    e = sum(log(marginal));
    if (n > 1 && abs((e - eold)/eold) < crit)
        return;
    else
        eold = e;
    end
    new_pr = (sum(posterior,2))';
    StateWeights = new_pr/Ndata;
    StateMu = bsxfun(@rdivide,(posterior*X')',new_pr);
    for j=1:M
        diffs = bsxfun(@minus,X,StateMu(:,j));
        tpost = (1/sqrt(new_pr(j)))*sqrt(posterior(j,:));
        diffs = bsxfun(@times,diffs,tpost);
        [~,tcov] = qr2(diffs',0);
        StateSqrtP(:,:,j) = tcov';
        if check
            if min(abs(diag(StateSqrtP(:,:,j)))) < MIN_COVAR_SQRT
                StateSqrtP(:,:,j) = init_covars(:,:,j);
            end
        end
    end
end
