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See the recommended documentation of this function

# phc

Markovian representation

### Syntax

`[H,F,G]=phc(hk,d,r)`

### Arguments

hk

hankel matrix

d

dimension of the observation

r

desired dimension of the state vector for the approximated model

H, F, G

relevant matrices of the Markovian model

### Description

Function which computes the matrices `H, F, G` of a Markovian representation by the principal hankel component approximation method, from the hankel matrix built from the covariance sequence of a stochastic process.

### Examples

```//This example may usefully be compared with the results from
//the 'levin' macro (see the corresponding help and example)
//
//We consider the process defined by two sinusoids (1Hz and 2 Hz)
//in additive Gaussian noise (this is the observation);
//the simulated process is sampled at 10 Hz.

t=0:.1:100;rand('normal');
y=sin(2*%pi*t)+sin(2*%pi*2*t);y=y+rand(y);plot(t,y)

//covariance of y

nlag=128;
c=corr(y,nlag);

//hankel matrix from the covariance sequence
//by taking greater n and m; try it to compare the results !

n=20;m=20;
h=hank(n,m,c);

//compute the Markov representation (mh,mf,mg)
//We just take here a state dimension equal to 4 :
//this is the rather difficult problem of estimating the order !
//Try varying ns !
//(the observation dimension is here equal to one)

ns=4;
[mh,mf,mg]=phc(h,1,ns);

//verify that the spectrum of mf contains the
//frequency spectrum of the observed process y
//(remember that y is sampled -in our example
//at 10Hz (T=0.1s) so that we need
//to retrieve the original frequencies through the log
//and correct scaling by the frequency sampling)

s=spec(mf);s=log(s);
s=s/2/%pi/.1;

//now we get the estimated spectrum
imag(s),```