filter

Forward recursion of Bayesian nonlinear non-Gaussian state-space model

Since R2023b

Syntax

[X,logL] = filter(Mdl,Y,params)
[X,logL] = filter(Mdl,Y,params,Name=Value)
[X,logL,Output,RND] = filter(___)

Description

filter estimates state-distribution moments of a Bayesian nonlinear non-Gaussian state-space model (bnlssm), conditioned on model parameters Θ, for each period of the specified response data by using importance sampling and resampling in the sequential Monte Carlo (SMC) framework. filter approximates the state filtering distribution and likelihood function by applying particles, or weighted random samples.

example

[X,logL] = filter(Mdl,Y,params) returns approximate state-distribution means X for each sampling time in the input response data Y and the corresponding loglikelihood logL resulting from performing forward recursion of, or filtering, the Bayesian nonlinear state-space model Mdl. filter evaluates the parameter map Mdl.ParamMap by using the vector of parameter values params.filter filters Y and particles, weighted random samples representing state values, through the model by using SMC.

example

[X,logL] = filter(Mdl,Y,params,Name=Value) specifies additional options using one or more name-value arguments. For example, filter(Mdl,Y,params,NumParticles=1e4,Resample="residual") specifies 1e4 particles for the SMC routine and to resample residuals.

example

[X,logL,Output,RND] = filter(___) additionally returns the following quantities using any of the input-argument combinations in the previous syntaxes: Output — Filtering results by sampling periodApproximate loglikelihood values associated with the input data, input parameters, and particlesFilter estimate of state-distribution meansFilter estimate of state-distribution covarianceCustom statisticsEffective sample sizeFlags indicating which data the software used to filterFlags indicating resamplingRND — Normal random variables generated by filter used to reproduce results or reuse random variates generated by a previous call of filter 

Examples

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This example uses simulated data to compute filter estimates of state distribution means of the following Bayesian nonlinear state-space model in equation. The state-space model contains two independent, stationary, autoregressive states each with a model constant. The observations are a nonlinear function of the states with Gaussian noise. The prior distribution of the parameters is flat. Symbolically, the system of equations is

$\left[\begin{array}{c}{x}_{t,1}\\ {x}_{t,2}\\ {x}_{t,3}\\ {x}_{t,4}\end{array}\right]=\left[\begin{array}{cccc}{\theta }_{1}& {\theta }_{2}& 0& 0\\ 0& 1& 0& 0\\ 0& 0& {\theta }_{3}& {\theta }_{4}\\ 0& 0& 0& 1\end{array}\right]\left[\begin{array}{c}{x}_{t-1,1}\\ {x}_{t-1,2}\\ {x}_{t-1,3}\\ {x}_{t-1,4}\end{array}\right]+\left[\begin{array}{cc}{\theta }_{5}& 0\\ 0& 0\\ 0& {\theta }_{6}\\ 0& 0\end{array}\right]\left[\begin{array}{c}{u}_{t,1}\\ {u}_{t,3}\end{array}\right]$

${y}_{t}=\mathrm{log}\left(\mathrm{exp}\left({x}_{t,1}-{\mu }_{1}\right)+\mathrm{exp}\left({x}_{t,3}-{\mu }_{3}\right)\right)+{\theta }_{7}{\epsilon }_{t}.$

${\mu }_{1}$ and ${\mu }_{3}$ are the unconditional means of the corresponding states. The initial distribution moments of each state are their unconditional mean and covariance.

Create a Bayesian nonlinear state-space model characterized by the system. The observation equation is in equation form, that is, the function composing the states is nonlinear and the innovation series ${\epsilon }_{\mathit{t}}$ is additive, linear, and Gaussian. The Local Functions section contains two functions required to specify the Bayesian nonlinear state-space model: the state-space model parameter mapping function and the prior distribution of the parameters. You can use the functions only within this script.

Mdl = bnlssm(@paramMap,@priorDistribution)
Mdl = bnlssm with properties: ParamMap: @paramMap ParamDistribution: @priorDistribution ObservationForm: "equation" Multipoint: [1x0 string] 

Mdl is a bnlssm model specifying the state-space model structure and prior distribution of the state-space model parameters. Because Mdl contains unknown values, it serves as a template for posterior analysis with observations.

Simulate a series of 100 observations from the following stationary 2-D VAR process.

$\begin{array}{l}{x}_{t,1}=1+0.9{x}_{t-1,1}+0.3{u}_{t,1}\\ {x}_{t,3}=-1+-0.75{x}_{t-1,3}+0.2{u}_{t,3},\end{array}$

where the disturbance series ${\mathit{u}}_{\mathit{t},\mathit{j}}$ are standard Gaussian random variables.

rng(1,"twister") % For reproducibility T = 100; thetatrue = [0.9; 1; -0.75; -1; 0.3; 0.2; 0.1]; MdlSim = varm(AR={diag(thetatrue([1 3]))},Covariance=diag(thetatrue(5:6).^2), ... Constant=thetatrue([2 4])); XSim = simulate(MdlSim,T);

Compose simulated observations using the following equation.

${y}_{t}=\mathrm{log}\left(\mathrm{exp}\left({x}_{t,1}-{\underset{}{\overset{‾}{x}}}_{1}\right)+\mathrm{exp}\left({x}_{t,3}-{\underset{}{\overset{‾}{x}}}_{3}\right)\right)+0.1{\epsilon }_{t},$

where the innovation series ${\epsilon }_{\mathit{t}}$ is a standard Gaussian random variable.

ysim = log(sum(exp(XSim - mean(XSim)),2)) + thetatrue(7)*randn(T,1);

To compute state estimates, the filter function requires response data and a model with known state-space model parameters. Choose a random set with the following constraints:

• ${\theta }_{1}$ and ${\theta }_{3}$ are within the unit circle. Use $\mathit{U}\left(-1,1\right)$ to generate values.

• ${\theta }_{2}$ and ${\theta }_{4}$ are real numbers. Use the $\mathit{N}\left(0,{3}^{2}\right)$ distribution to generate values.

• ${\theta }_{5}$, ${\theta }_{6}$, and ${\theta }_{7}$ are positive real numbers. Use the ${\chi }_{1}^{2}$ distribution to generate values.

theta13 = (-1+(1-(-1)).*rand(2,1)); theta24 = 3*randn(2,1); theta567 = chi2rnd(1,3,1); theta = [theta13(1); theta24(1); theta13(2); theta24(2); theta567];

Compute filtered state estimates and corresponding loglikelihood by passing the Bayesian nonlinear model, simulated data, and parameter values to filter.

[FilterX,logL] = filter(Mdl,ysim,theta); size(FilterX)
ans = 1×2 100 4 
logL
logL = -134.1053 

FilterX is a 100-by-4 matrix of filter state estimates, with rows corresponding to periods in the sample and columns corresponding to the state variables. logL is the approximate loglikelihood function estimate evaluated at the data and parameter values.

Compare the loglikelihood logL and the loglikelihood computed using $\Theta$ from the data simulation.

[FilterXSim,logLSim] = filter(Mdl,ysim,thetatrue); logLSim
logLSim = -0.4078 

logLSim > logL, suggesting that the model evaluated at thetaSim has the better fit.

Plot the two sets of filter state estimates with the true state values.

figure tiledlayout(2,1) nexttile plot([FilterX(:,1) FilterXSim(:,1) XSim(:,1)]) title("x_{t,1}") legend("Filter State, random \theta","Filter State, true \theta","XSim") nexttile plot([FilterX(:,3) FilterXSim(:,3) XSim(:,2)]) title("x_{t,3}") legend("Filter State, random \theta","Filter State, true \theta","XSim")

The filter state estimates using the true value of $\Theta$ and the simulated state paths are close. The filter state estimates are far from the simulated state paths.

Local Functions

These functions specify the state-space model parameter mappings, in equation form, and log prior distribution of the parameters.

function [A,B,C,D,Mean0,Cov0,StateType] = paramMap(theta) A = @(x)blkdiag([theta(1) theta(2); 0 1],[theta(3) theta(4); 0 1])*x; B = [theta(5) 0; 0 0; 0 theta(6); 0 0]; C = @(x)log(exp(x(1)-theta(2)/(1-theta(1))) + ... exp(x(3)-theta(4)/(1-theta(3)))); D = theta(7); Mean0 = [theta(2)/(1-theta(1)); 1; theta(4)/(1-theta(3)); 1]; Cov0 = diag([theta(5)^2/(1-theta(1)^2) 0 theta(6)^2/(1-theta(3)^2) 0]); StateType = [0; 1; 0; 1]; % Stationary state and constant 1 processes end function logprior = priorDistribution(theta) paramconstraints = [(abs(theta([1 3])) >= 1) (theta(5:7) <= 0)]; if(sum(paramconstraints)) logprior = -Inf; else logprior = 0; % Prior density is proportional to 1 for all values % in the parameter space. end end

This example shows how to use filter and Bayesian optimization to calibrate a Bayesian nonlinear state-space. Consider this nonlinear state-space model.

$\begin{array}{l}{x}_{t}={\theta }_{1}{x}_{t-1}+{\theta }_{2}{u}_{t}\\ {y}_{t}={e}^{{\theta }_{3}{x}_{t}}+{\theta }_{4}+{\theta }_{5}{\epsilon }_{t},\end{array}$

where $\theta$ has a flat prior and the series ${\mathit{u}}_{\mathit{t}}$ and ${\epsilon }_{\mathit{t}}$ are standard Gaussian random variables.

Simulate Series

Simulate a series of 100 observations from the following stationary 2-D VAR process.

$\begin{array}{l}{x}_{t}=0.5{x}_{t-1}+0.6{u}_{t}\\ {y}_{t}={e}^{-{x}_{t}}+2+0.75{\epsilon }_{t}.\end{array}$

where the series ${\mathit{u}}_{\mathit{t}}$ and ${\epsilon }_{\mathit{t}}$ are standard Gaussian random variables.

rng(10,"twister") % For reproducibility T = 100; thetaDGP = [0.5; 0.6; -1; 2; 0.75]; numparams = numel(thetaDGP); MdlSim = arima(AR=thetaDGP(1),Variance=sqrt(thetaDGP(2)), ... Constant=0); xsim = simulate(MdlSim,T); ysim = exp(thetaDGP(3).*xsim) + thetaDGP(4) + thetaDGP(5)*randn(T,1);

Create Bayesian Nonlinear Model

Create a Bayesian nonlinear state-space model. The Local Functions section contains two functions required to specify the Bayesian nonlinear state-space model: the state-space model parameter mapping function and the prior distribution of the parameters. You can use the functions only within this script. Specify Multipoint=["A" "C"] because the state-transition and measurement-sensitivity parameter mappings in paraMap can evaluate multiple particles simultaneously.

Mdl = bnlssm(@paramMap,@priorDistribution,Multipoint=["A" "C"]);

Perform Random Exploration

One way to explore the parameter space for the point that maximizes the likelihood is by random exploration:

1. Randomly generated set of parameters.

2. Compute the loglikelihood for that set.

3. Repeat steps 1 and 2 many times.

4. Choose the set of parameters that yields the largest loglikelihood.

Perform random exploration for 500 epochs. Choose a random set using the following arbitrary recipe:

• ${\theta }_{1}\sim \text{\hspace{0.17em}}\mathit{U}\left(-1,1\right)$.

• ${\theta }_{2}$ and ${\theta }_{5}$ are ${\chi }_{1}^{2}$.

• ${\theta }_{3}$ and ${\theta }_{4}$ are$\mathit{N}\left(0,2\right)$.

numepochs = 500; numparticles = 10000; theta = NaN(numparams,numepochs); logL = NaN(numepochs,1); for j = 1:numepochs theta(:,j) = [(-1+(1-(-1)).*rand(1,1)); chi2rnd(1,1,1); ... 2*randn(2,1); chi2rnd(1,1,1);]; [~,logL(j)] = filter(Mdl,ysim,theta(:,j),NumParticles=numparticles); end

Choose the set of parameters that maximizes the loglikelihood.

[bestlogL,idx] = max(logL); bestTheta = theta(:,idx); [bestTheta thetaDGP]
ans = 5×2 0.4934 0.5000 0.4843 0.6000 -1.4906 -1.0000 1.6399 2.0000 0.7260 0.7500 

The values that maximize the likelihood are fairly close to the values that generated the data.

Perform Bayesian Optimization

Bayesian optimization requires you to specify which variables require optimization and their support. To do this, use optimizableVariable to provide a name to the variable and its support. This example limits the support of each variable to a small interval; you must experiment with the support for your application.

thetaOp(5) = optimizableVariable("theta5",[0,2]); thetaOp(1) = optimizableVariable("theta1",[0,0.9]); thetaOp(2) = optimizableVariable("theta2",[0,2]); thetaOp(3) = optimizableVariable("theta3",[-3,0]); thetaOp(4) = optimizableVariable("theta4",[-3,3]);

thetaOp is an optimizableVariable object specifying the name and support of each variable of $\theta$.

bayesopt accepts a function handle to the function that you want to minimize with respect to one argument $\theta$ and the optimizable variable specifications. Therefore, provide the function handle neglog, which is associated with the negative loglikelihood function filterneglogl located in Local Functions. Specify the Expected Improvement Plus acquisition function and an exploration ratio of 1.

neglogl = @(var)filterneglogl(var,Mdl,ysim,numparticles); rng(1,"twister") results = bayesopt(neglogl,thetaOp,AcquisitionFunctionName="expected-improvement-plus", ... ExplorationRatio=1);
|==================================================================================================================================================| | Iter | Eval | Objective | Objective | BestSoFar | BestSoFar | theta1 | theta2 | theta3 | theta4 | theta5 | | | result | | runtime | (observed) | (estim.) | | | | | | |==================================================================================================================================================| | 1 | Best | 1035.3 | 0.25005 | 1035.3 | 1035.3 | 0.0761 | 0.51116 | -0.78522 | -2.272 | 0.44888 | | 2 | Best | 331.41 | 0.1235 | 331.41 | 372.47 | 0.5021 | 0.8957 | -0.11662 | -1.1518 | 1.8165 | | 3 | Best | 249.01 | 0.14956 | 249.01 | 249.04 | 0.67392 | 0.54178 | -2.091 | -0.82814 | 0.63451 | | 4 | Accept | 381.54 | 0.19028 | 249.01 | 249.17 | 0.74179 | 1.8872 | -1.9159 | -2.5691 | 1.596 | | 5 | Best | 183.98 | 0.1731 | 183.98 | 184.25 | 0.60884 | 0.17723 | -1.2634 | 2.9947 | 1.8405 | | 6 | Best | 182.33 | 0.17774 | 182.33 | 183.34 | 0.60034 | 1.5367 | -2.5329 | 2.5026 | 0.91717 | | 7 | Accept | 247.19 | 0.28516 | 182.33 | 183.13 | 0.89999 | 0.70185 | -1.537 | -1.1312 | 0.7421 | | 8 | Accept | 274.28 | 0.2778 | 182.33 | 174.46 | 0.59232 | 1.7554 | -1.597 | 0.78832 | 0.13249 | | 9 | Best | 177.47 | 0.20017 | 177.47 | 177.45 | 0.89577 | 0.097083 | -0.37012 | 2.9984 | 1.5053 | | 10 | Accept | 1055.7 | 0.26589 | 177.47 | 177.48 | 0.77811 | 1.0092 | -2.4812 | 2.9977 | 0.16125 | | 11 | Best | 172.82 | 0.13244 | 172.82 | 172.81 | 0.067163 | 0.45713 | -2.0117 | 2.3819 | 1.3694 | | 12 | Accept | 182.61 | 0.20778 | 172.82 | 172.81 | 0.1524 | 1.3513 | -1.2805 | 1.7846 | 0.96486 | | 13 | Accept | 293.62 | 0.36298 | 172.82 | 172.8 | 0.020503 | 0.68288 | -0.90552 | -0.39937 | 1.0775 | | 14 | Accept | 213.33 | 0.17319 | 172.82 | 172.79 | 0.66738 | 1.6267 | -2.0922 | 1.7538 | 1.9997 | | 15 | Accept | 247.32 | 0.17942 | 172.82 | 172.82 | 0.39009 | 1.9718 | -2.4707 | 1.0908 | 1.5419 | | 16 | Accept | 212.29 | 0.21339 | 172.82 | 172.78 | 0.89876 | 1.7163 | -0.71511 | 0.54894 | 0.80624 | | 17 | Accept | 173.36 | 0.13541 | 172.82 | 172.81 | 0.89453 | 1.3794 | -0.97214 | 2.1219 | 1.2087 | | 18 | Accept | 222.99 | 0.28253 | 172.82 | 172.79 | 0.0036001 | 1.4357 | -0.62811 | 0.50127 | 0.4746 | | 19 | Accept | 247.67 | 0.17279 | 172.82 | 172.82 | 0.16438 | 1.2307 | -2.1934 | 0.44716 | 1.9995 | | 20 | Best | 164.01 | 0.21078 | 164.01 | 163.91 | 0.75079 | 0.35782 | -2.5596 | 2.682 | 1.1664 | |==================================================================================================================================================| | Iter | Eval | Objective | Objective | BestSoFar | BestSoFar | theta1 | theta2 | theta3 | theta4 | theta5 | | | result | | runtime | (observed) | (estim.) | | | | | | |==================================================================================================================================================| | 21 | Accept | 469.58 | 0.21565 | 164.01 | 163.91 | 0.22341 | 1.0454 | -0.35525 | -2.9856 | 1.9965 | | 22 | Accept | 272.86 | 0.30881 | 164.01 | 163.95 | 0.10452 | 1.885 | -2.5501 | 1.2019 | 0.61558 | | 23 | Accept | 177.98 | 0.16109 | 164.01 | 163.96 | 0.58745 | 0.30598 | -2.6784 | 2.3311 | 1.7068 | | 24 | Accept | 230.65 | 0.22395 | 164.01 | 163.93 | 0.88893 | 1.9352 | -0.68612 | 0.1287 | 0.40965 | | 25 | Accept | 176.39 | 0.2195 | 164.01 | 164.84 | 0.036535 | 0.96617 | -1.4887 | 2.3525 | 1.0554 | | 26 | Accept | 170.33 | 0.14586 | 164.01 | 164.25 | 0.69849 | 1.2507 | -0.93683 | 3 | 1.1078 | | 27 | Best | 163.98 | 0.24402 | 163.98 | 164.04 | 0.8791 | 0.56387 | -1.6589 | 2.9695 | 1.268 | | 28 | Accept | 185.57 | 0.22644 | 163.98 | 164.07 | 0.79305 | 0.32402 | -0.35449 | 2.9236 | 1.9999 | | 29 | Best | 162.03 | 0.13255 | 162.03 | 162.19 | 0.89512 | 0.30185 | -1.2532 | 2.6889 | 1.3122 | | 30 | Accept | 214.85 | 0.21201 | 162.03 | 162.3 | 0.89002 | 0.75265 | -1.1744 | -0.36792 | 0.75282 | __________________________________________________________ Optimization completed. MaxObjectiveEvaluations of 30 reached. Total function evaluations: 30 Total elapsed time: 23.0234 seconds Total objective function evaluation time: 6.2538 Best observed feasible point: theta1 theta2 theta3 theta4 theta5 _______ _______ _______ ______ ______ 0.89512 0.30185 -1.2532 2.6889 1.3122 Observed objective function value = 162.0337 Estimated objective function value = 162.2987 Function evaluation time = 0.13255 Best estimated feasible point (according to models): theta1 theta2 theta3 theta4 theta5 _______ _______ _______ ______ ______ 0.89512 0.30185 -1.2532 2.6889 1.3122 Estimated objective function value = 162.2987 Estimated function evaluation time = 0.17874 

results is a BayesianOptmization object containing properties summarizing the results of Bayesian optimization.

Extract the value that minimizes the negative loglikelihood from results by using bestPoint.

bestPoint(results)
ans=1×5 table theta1 theta2 theta3 theta4 theta5 _______ _______ _______ ______ ______ 0.89512 0.30185 -1.2532 2.6889 1.3122 

The results are fairly close to the values that generated the data.

Local Functions

These functions specify the state-space model parameter mappings, in equation form, and the log prior distribution of the parameters, and they compute the negative loglikelihood of the model.

function [A,B,C,D,Mean0,Cov0,StateType] = paramMap(theta) A = @(x)theta(1)*x; B = theta(2); C = @(x)exp(theta(3).*x) + theta(4); D = theta(5); Mean0 = 0; Cov0 = 1; StateType = 0; % Stationary state process end function logprior = priorDistribution(theta) paramconstraints = [abs(theta(1)) >= 1; theta([2 4]) <= 0]; if(sum(paramconstraints)) logprior = -Inf; else logprior = 0; % Prior density is proportional to 1 for all values % in the parameter space. end end function neglogL = filterneglogl(theta,mdl,resp,np) theta = table2array(theta); [~,logL] = filter(mdl,resp,theta,NumParticles=np); neglogL = -logL; end

When a set of state-space parameters are close in space, they can yield sufficiently different loglikelihood function values. This phenomenon is due to Monte Carlo sampling error. To address Monte Carlo sampling error so that comparisons among of loglikelihoods evaluated from similar parameter values during calibration is more fair, apply Hilbert sorting to the particles. This example reproduces results to show the effects of sorting particles.

Consider this simple linear Gaussian state-space model.

$\begin{array}{l}{x}_{t}={x}_{t-1}+{u}_{t}\\ {y}_{t}={x}_{t}+\theta {\epsilon }_{t},\end{array}$

where the error series ${\mathit{u}}_{\mathit{t}}$ and ${\epsilon }_{\mathit{t}}$ are standard Gaussian random variables.

Assuming that the observation innovation variance is 1, simulate a response series of length 300 from the model. The Local Functions section contains the parameter mapping.

T = 300; rng(1,"twister") % For reproducibility MdlTrue = ssm(@paramMap); y = simulate(MdlTrue,T,Params=1);

Specify two values of $\theta$, at which to evaluate the model, that are close to each other.

theta1 = 0.5; theta2 = 0.5 + 1e-7;

Evaluate the true loglikelihood at each value of $\theta$ by using the filter function of ssm.

[~,logL1] = filter(MdlTrue,y,Params=theta1); [~,logL2] = filter(MdlTrue,y,Params=theta2); [logL1 logL2 abs(logL1-logL2)]
ans = 1×3 -627.5987 -627.5987 0.0000 

The values of $\theta$ yield practically identical loglikelihood values.

Create a Bayesian nonlinear state-space model using the parameter mapping and log prior density functions.

Mdl = bnlssm(@paramMap,@priorDistribution);

Evaluate the Bayesian loglikelihood at the values of $\theta$.

[~,logL1] = filter(Mdl,y,theta1); [~,logL2] = filter(Mdl,y,theta2); [logL1 logL2 abs(logL1-logL2)]
ans = 1×3 -629.1780 -628.4513 0.7267 

Despite the arguments being close, there is a difference between their corresponding loglikelihoods. The difference can be attributed to Monte Carlo sampling.

Evaluate the loglikelihood at the value of ${\theta }_{1}$ again using the Bayesian model, but return the normal random variates to reproduce subsequent calls of filter.

[~,logL1,~,rnd] = filter(Mdl,y,theta1); rnd
rnd = struct with fields: Autocorrelation: 1 Time: 300 Initial: [0x1000 double] Proposal: {300x1 cell} Resample: {300x1 cell} ResampleFlag: [300x1 logical] 

rnd is a structure array containing sampling information.

Evaluate the loglikelihood at the value of ${\theta }_{2}$ again, but use the same normal random variates used to evaluate the loglikelihood at ${\theta }_{1}$.

[~,logL2] = filter(Mdl,y,theta2,RND=rnd); [logL1 logL2 abs(logL1-logL2)]
ans = 1×3 -629.3398 -629.2906 0.0491 

Still, the loglikelihoods are different.

Evaluate the loglikelihood at the values of $\theta$ by passing them and normal variates to filter again. Apply Hilbert sorting to the particles.

[~,logL1,~,rnd] = filter(Mdl,y,theta1,SortParticles=true); [~,logL2] = filter(Mdl,y,theta2,RND=rnd,SortParticles=true); [logL1 logL2 abs(logL1-logL2)]
ans = 1×3 -631.4925 -631.4925 0.0000 

Although the loglikelihood magnitudes are different from the frequentist state-space model, they are practically the same for values of $\theta$ that are very close to each other.

Local Functions

These functions specify the state-space model parameter mapping, in equation form, and log prior distribution of the parameter.

function [A,B,C,D,mean0,Cov0] = paramMap(theta) A = 1; B = 1; C = 1; D = theta; mean0 = 0; Cov0 = 0; end function logprior = priorDistribution(theta) paramconstraints = theta <= 0; if(sum(paramconstraints)) logprior = -Inf; else logprior = 0; % Prior density is proportional to 1 for all values % in the parameter space. end end

Input Arguments

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Bayesian nonlinear state-space model, specified as a bnlssm model object created by the bnlssm function.

The function handles of the properties Mdl.ParamDistribution and Mdl.ParamMap determine the prior and the data likelihood, respectively. filter evaluates Mdl.ParamMap at the input params.

Observed response data, specified as a numeric matrix or a cell vector of numeric vectors.

• If Mdl is time invariant with respect to the observation equation, Y is a T-by-n matrix. Each row of the matrix corresponds to a period and each column corresponds to a particular observation in the model. T is the sample size and n is the number of observations per period. The last row of Y contains the latest observations.

• If Mdl is time varying with respect to the observation equation, Y is a T-by-1 cell vector. Y{t} contains an nt-dimensional vector of observations for period t, where t = 1, ..., T. For linear observation models, the corresponding dimensions of the coefficient matrices, outputs of Mdl.ParamMap, C{t}, and D{t} must be consistent with the matrix in Y{t} for all periods. For nonlinear observation models, the dimensions of the inputs and outputs associated with the observations must be consistent. Regardless of model type, the last cell of Y contains the latest observations.

NaN elements indicate missing observations. For details on how the Kalman filter accommodates missing observations, see Algorithms.

Data Types: double | cell

State-space model parameters Θ to evaluate the parameter mapping Mdl.ParamMap, specified as a numparams-by-1 numeric vector. Elements of params0 must correspond to the elements of the first input arguments of Mdl.ParamMap and Mdl.ParamDistribution.

Data Types: double

Name-Value Arguments

Specify optional pairs of arguments as Name1=Value1,...,NameN=ValueN, where Name is the argument name and Value is the corresponding value. Name-value arguments must appear after other arguments, but the order of the pairs does not matter.

Before R2021a, use commas to separate each name and value, and enclose Name in quotes.

Example: filter(Mdl,Y,params,NumParticles=1e4,Resample="residual") specifies 1e4 particles for the SMC routine and to resample residuals.

Number of particles for SMC, specified as a positive integer.

Example: NumParticles=1e4

Data Types: double

SMC resampling method, specified as a value in this table.

ValueDescription
"multinomial"At time t, the set of previously generated particles (parent set) follows a standard multinomial distribution, with probabilities proportional to their weights. An offspring set is resampled with replacement from the parent set [1].
"residual"Residual sampling, a modified version of multinomial resampling that can produce an estimator with lower variance than the multinomial resampling method [6].
"systematic"Systematic sampling, which produces an estimator with lower variance than the multinomial resampling method [4].

Resampling methods downsample insignificant particles to achieve a smaller estimator variance than if no resampling is performed and to avoid sampling from a degenerate proposal [5].

Example: Resample="residual"

Data Types: char | string

Effective sample size threshold, below which filter resamples particles, specified as a nonnegative scalar. For more details, see [5], Ch. 12.3.3.

Tip

• To resample during every period, set Cutoff=numparticles, where numparticles is the value of the NumParticles name-value argument.

• To avoid resampling, set Cutoff=0.

Example: Cutoff=0.75*numparticles

Data Types: double

Flag for sorting particles before resampling, specified as a value in this table.

ValueDescription
truefilter sorts the generated particles before resampling them.
falsefilter does not sort the generated particles.

When you set SortPartiles=true, filter uses Hilbert sorting during the SMC routine to sort the particles. This action can reduce Monte Carlo variation, which is useful when you compare loglikelihoods resulting from evaluating several params arguments that are close to each other [3]. However, the sorting routine requires more computation resources, and can slow down computations, particularly in problems with a high-dimensional state variable.

Example: SortParticles=true

Data Types: logical

Custom function of particles and normalized weights for monitoring the SMC, specified as a function handle (@customstats).

The associated function signature must have this form:

function stat = customStatistics(particles,weights)

where:

• customStatistics is the function name, which you choose.

• particles is the mt-by-NumParticles chosen particles at forward-filtering time t of the SMC routine.

• weights is the 1-by-NumParticles normalized importance weights, corresponding to particles, at forward-filtering time t of the SMC routine.

• stat is the evaluation of customStatistics at particles and weights at forward-filtering time t of the SMC routine. For example, stat can be a numeric vector or structure array.

Example: You can return the particles and weights at each filtering step by setting CustomStatistics=@customstats, where you write customstats to return those inputs in a structure array: stat.p = particles; stat.w = weights;

Data Types: function_handle

Previously generated normal random numbers to reproduce filter results, specified as the RND output, a structure array, of previous filter call.

The default is an empty structure array, which causes filter to generate new random numbers.

Data Types: struct

Output Arguments

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Approximate filtered state estimates E(xt|y1,…,yt), returned as a T-by-m numeric matrix or a T-by-1 cell vector of numeric vectors.

Each row corresponds to a time point in the sample. The last row contains the latest filtered states.

For time-invariant models, filter returns a matrix. Each column corresponds to a state variable xt.

If Mdl is time varying, X is a cell vector. Cell t contains a column vector of filtered state estimates with length mt. Each column corresponds to a state variable.

Approximate loglikelihood function value log p(y1,…,yT).

The loglikelihood value has noise induced by SMC.

Missing observations do not contribute to the loglikelihood.

SMC filtering results by period, returned as a structure array.

Output is a T-by-1 structure, where element t corresponds to the filtering result for time t.

FieldDescriptionEstimate/Approximation of
LogLikelihoodScalar approximate loglikelihood objective function value log p(yt|y1,…,yt)
FilteredStatesmt-by-1 vector of approximate filtered state estimates$E\left({x}_{t}|{y}_{1},...,{y}_{t}\right)$
FilteredStatesCovmt-by-mt variance-covariance matrix of filtered states$Var\left({x}_{t}|{y}_{1},...,{y}_{t}\right)$
CustomStatisticsDetermined by CustomStatistics settingDetermined by CustomStatistics setting
EffectiveSampleSizeEffective sample size for importance sampling, a scalar in [0,NumParticles]N/A
DataUsedht-by-1 flag indicating whether the software filters using a particular observation. For example, if observation j at time t is a NaN, element j in DataUsed at time t is 0.N/A
ResampledFlag indicating whether filter resampled particlesN/A

SMC normal random variate information to reproduce results of subsequent calls of filter, returned as a structure array.

To reproduce subsequent filtering results or to use the same random variates as previously used:

1. Set a random number seed by using the rng function.

2. Return RND when you call filter.

3. Set the RND name-value argument to the output RND when you call filter again to reproduce results or reuse the random variates in RND.

Tips

Unless you set Cutoff=0, filter resamples particles according to the specified resampling method Resample. Although resampling particles with high weights improves the results of the SMC, you should also allow the sampler traverse the proposal distribution to obtain novel, high-weight particles. To do this, experiment with Cutoff.

Algorithms

filter accommodates missing data by not updating filtered state estimates corresponding to missing observations. In other words, suppose there is a missing observation at period t. Then, the state forecast for period t based on the previous t – 1 observations and filtered state for period t are equivalent.

References

[1] Andrieu, Christophe, Arnaud Doucet, and Roman Holenstein. "Particle Markov Chain Monte Carlo Methods." Journal of the Royal Statistical Society Series B: Statistical Methodology 72 (June 2010): 269–342. https://doi.org/10.1111/j.1467-9868.2009.00736.x.

[2] Andrieu, Christophe, and Gareth O. Roberts. "The Pseudo-Marginal Approach for Efficient Monte Carlo Computations." Ann. Statist. 37 (April 2009): 697–725. https://dx.doi.org/10.1214/07-AOS574.

[3] Deligiannidis, George, Arnaud Doucet, and Michael Pitt. "The Correlated Pseudo-Marginal Method." Journal of the Royal Statistical Society, Series B: Statistical Methodology 80 (June 2018): 839–870. https://doi.org/10.1111/rssb.12280.

[4] Carpenter, James, Peter Clifford, and Paul Fearnhead. "Improved Particle Filter for Nonlinear Problems." IEE Proceedings - Radar, Sonar and Navigation 146 (February 1999): 2–7. https://doi.org/10.1049/ip-rsn:19990255.

[5] Durbin, J, and Siem Jan Koopman. Time Series Analysis by State Space Methods. 2nd ed. Oxford: Oxford University Press, 2012.

[6] Liu, Jun, and Rong Chen. "Sequential Monte Carlo Methods for Dynamic Systems." Journal of the American Statistical Association 93 (September 1998): 1032–1044. https://dx.doi.org/10.1080/01621459.1998.10473765.

Version History

Introduced in R2023b