root/bdm/estim/arx.h @ 97

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ARX model : estimation and brute-force structure estimation

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1/*!
2  \file
3  \brief Bayesian Filtering for generalized autoregressive (ARX) model
4  \author Vaclav Smidl.
5
6  -----------------------------------
7  BDM++ - C++ library for Bayesian Decision Making under Uncertainty
8
9  Using IT++ for numerical operations
10  -----------------------------------
11*/
12
13#ifndef AR_H
14#define AR_H
15
16#include <itpp/itbase.h>
17#include "../stat/libFN.h"
18#include "../stat/libEF.h"
19
20using namespace itpp;
21
22/*!
23* \brief Linear Autoregressive model with Gaussian noise
24
25Regression of the following kind:
26\f[
27y_t = \theta_1 \psi_1 + \theta_2 + \psi_2 +\ldots + \theta_n \psi_n + r e_t
28\f]
29where unknown parameters \c rv are \f$[\theta r]\f$, regression vector \f$\psi=\psi(y_{1:t},u_{1:t})\f$ is a known function of past outputs and exogeneous variables \f$u_t\f$. Distrubances \f$e_t\f$ are supposed to be normally distributed:
30\f[
31e_t \sim \mathcal{N}(0,1).
32\f]
33
34Extension for time-variant parameters \f$\theta_t,r_t\f$ may be achived using exponential forgetting (Kulhavy and Zarrop, 1993). In such a case, the forgetting factor \c frg \f$\in <0,1>\f$ should be given in the constructor. Time-invariant parameters are estimated for \c frg = 1.
35*/
36class ARX: public BM {
37protected:
38        //! Posterior estimate of \f$\theta,r\f$ in the form of Normal-inverse Wishart density
39        egiw est;
40        //! cached value of est.V
41        ldmat &V;
42        //! cached value of est.nu
43        double &nu;
44        //! forgetting factor
45        double frg;
46        //! cached value of lognc() in the previous step (used in evaluation of \c ll )
47        double last_lognc;
48public:
49        //! Full constructor
50        ARX (RV &rv, mat &V0, double &nu0, double frg0=1.0) : BM(rv),est(rv,V0,nu0), V(est._V()), nu(est._nu()), frg(frg0){last_lognc=est.lognc();};
51        //! Here \f$dt = [y_t psi_t] \f$.
52        void bayes ( const vec &dt );
53        epdf& _epdf() {return est;}
54        //! Brute force structure estimation.\return indeces of accepted regressors.
55        ivec structure_est(egiw Eg0);
56};
57
58
59#endif // AR_H
60
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