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

% Evaluates the likelihood of a non-linear model approximating the predictive (prior) and filtered (posterior) densities for state variables
% by a Kalman filter.
%
% INPUTS:
% - Reduced_Form     [struct]    MATLAB's structure describing the reduced form model.
% - Y                [double]    matrix of original observed variables.
% - start            [double]    structural parameters.
% - ParticleOptions  [struct]    MATLAB's structure describing options concerning particle filtering.
% - ThreadsOptions   [struct]    MATLAB's structure.
% - 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 distribution approximation is done by:
%  - a spherical-radial cubature (ref: Arasaratnam & Haykin, 2009).
%  - a scaled unscented transform cubature (ref: Julier & Uhlmann 1995)
%  - Monte-Carlo draws from a multivariate gaussian distribution.
%
% First and second moments of prior and posterior state densities are computed
% from the resulting nodes/particles and allows to generate new distributions at the
% following observation.
% Pros: The use of nodes is much faster than Monte-Carlo Gaussian particle and standard particles
%       filters since it treats a lesser number of particles.
% Cons:
%   1. Application a linear projection formula in a nonlinear context.
%   2. Parameter estimations may be biased if the model is truly non-gaussian since predictive and
%       filtered densities are unimodal.
%
% 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('nonlinear_kalman_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);

% compute gaussian quadrature nodes and weights on states and shocks
if ParticleOptions.proposal_approximation.montecarlo
    set_dynare_seed_local_options([],false,'default');
    nodes = randn(ParticleOptions.number_of_particles,number_of_state_variables+number_of_structural_innovations);
    weights = 1/ParticleOptions.number_of_particles;
    weights_c = weights;
elseif ParticleOptions.proposal_approximation.cubature
    [nodes,weights] = spherical_radial_sigma_points(number_of_state_variables+number_of_structural_innovations);
    weights_c = weights;
elseif ParticleOptions.proposal_approximation.unscented
    [nodes,weights,weights_c] = unscented_sigma_points(number_of_state_variables+number_of_structural_innovations,ParticleOptions.unscented);
else
    error('Estimation: This approximation for the proposal is not implemented or unknown!')
end

% Get covariance matrices
H = ReducedForm.H;
H_lower_triangular_cholesky = chol(H)' ;
Q_lower_triangular_cholesky = chol(ReducedForm.Q)';

% Get initial condition for the state vector.
StateVectorMean = ReducedForm.StateVectorMean;
StateVectorVarianceSquareRoot = chol(ReducedForm.StateVectorVariance)';

% Initialization of the likelihood.
lik  = NaN(sample_size,1);
LIK  = NaN;

for t=1:sample_size
    xbar = [StateVectorMean ; zeros(number_of_structural_innovations,1) ] ;
    sqr_Px = [StateVectorVarianceSquareRoot zeros(number_of_state_variables,number_of_structural_innovations);
               zeros(number_of_structural_innovations,number_of_state_variables) Q_lower_triangular_cholesky];
    sigma_points = bsxfun(@plus,xbar,sqr_Px*(nodes'));
    StateVectors = sigma_points(1:number_of_state_variables,:);
    epsilon = sigma_points(number_of_state_variables+1:number_of_state_variables+number_of_structural_innovations,:);
    tmp=iterate_law_of_motion(StateVectors,epsilon,ReducedForm,M_,options_,ReducedForm.use_k_order_solver,false);

    PredictedStateMean = tmp(mf0,:)*weights ;
    PredictedObservedMean = tmp(mf1,:)*weights;
    if ParticleOptions.proposal_approximation.cubature || ParticleOptions.proposal_approximation.montecarlo
        PredictedStateMean = sum(PredictedStateMean,2);
        PredictedObservedMean = sum(PredictedObservedMean,2);
        dState = bsxfun(@minus,tmp(mf0,:),PredictedStateMean)'.*sqrt(weights);
        dObserved = bsxfun(@minus,tmp(mf1,:),PredictedObservedMean)'.*sqrt(weights);
        big_mat = [dObserved  dState ; [H_lower_triangular_cholesky zeros(number_of_observed_variables,number_of_state_variables)] ];
        [~, mat] = qr2(big_mat,0);
        mat = mat';
        PredictedObservedVarianceSquareRoot = mat(1:number_of_observed_variables,1:number_of_observed_variables);
        CovarianceObservedStateSquareRoot = mat(number_of_observed_variables+(1:number_of_state_variables),1:number_of_observed_variables);
        StateVectorVarianceSquareRoot = mat(number_of_observed_variables+(1:number_of_state_variables),number_of_observed_variables+(1:number_of_state_variables));
        PredictionError = Y(:,t) - PredictedObservedMean;
        StateVectorMean = PredictedStateMean + (CovarianceObservedStateSquareRoot/PredictedObservedVarianceSquareRoot)*PredictionError;
    else
        dState = bsxfun(@minus,tmp(mf0,:),PredictedStateMean);
        dObserved = bsxfun(@minus,tmp(mf1,:),PredictedObservedMean);
        PredictedStateVariance = dState*diag(weights_c)*dState';
        PredictedObservedVariance = dObserved*diag(weights_c)*dObserved' + H;
        PredictedStateAndObservedCovariance = dState*diag(weights_c)*dObserved';
        PredictionError = Y(:,t) - PredictedObservedMean;
        KalmanFilterGain = PredictedStateAndObservedCovariance/PredictedObservedVariance;
        StateVectorMean = PredictedStateMean + KalmanFilterGain*PredictionError;
        StateVectorVariance = PredictedStateVariance - KalmanFilterGain*PredictedObservedVariance*KalmanFilterGain';
        [StateVectorVarianceSquareRoot, p]= chol(StateVectorVariance,'lower');
        if p
            LIK=-Inf;
            lik(t)=-Inf;
            return
        end
        [~, p]= chol(PredictedObservedVariance,'lower');
        if p
            LIK=-Inf;
            lik(t)=-Inf;
            return
        end
    end
    lik(t) = log( sum(probability2(Y(:,t),H_lower_triangular_cholesky,tmp(mf1,:)).*weights,1) ) ;
end

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