In *system identification*, we infer the parameters of a
stochastic dynamical system
of a certain type, i.e. usually
one with feedback,
so that we can
e.g. simulate it, or deconvolve it to find the inputs and hidden state, maybe using
state filters.
In statistical terms, this is the parameter inference problem for dynamical systems.

Moreover, it totally works without Gaussian noise;
that’s just *convenient* in optimal linear filtering,
Kalman filtering isn’t rocket science, after all.
Also, mathematically Gaussian is a useful crutch if you decide to go to a continuous time index,
cf Gaussian processes.

This is the offline version. For online, recusive estimates, see recursive estimation, which will be handled separately.

## Intros

Oppenheim and Verghese, Signals, Systems, and Inference is free online.

Martin (Mart99a):

Consider the basic autoregressive model,

Estimating AR(p) coefficients:

The [power] spectrum is easily obtained from [the above] as

\begin{align*} P(f) = \frac{\sigma^2}{|1+ \sum_{j=1}^pa_jz^{-1}|^2},\\ z=\exp 2\pi if\delta t \end{align*}with \(\delta t\) the intersample spacing.[…] for any given set of data, we need to be able to estimate the AR coeficients \(\{a_j\}_{j=1}^N\) conveniently. Three methods for achieving this are the Yule-Walker, Burg and Covariance methods. The Yule-Walker technique uses the sample autocovariance to obtain the coefficients; the Covariance method defines, for a set of numbers \(\mathbf{a}=\{a_j\}_{j=1}^N,\) a quantity known as the total forward and backward prediction error power:

\begin{equation*} E(Y,\mathbf{a}) = \frac{1}{2(N-p)}\sum_{n=p+1}^N\left\{ \left|Y(n)+\sum{j=1}^pa_jY(n-p)\right|^2 + \left|Y(n-p)+\sum{j=1}^pa^*_jY(n-p+j)\right|^2 \right\} \end{equation*}and minimises this w.r.t. \(\mathbf{a}\). As \(E(Y, \mathbf{a})\) is a quadratic function of \(\mathbf{a}\), \(\partial E(Y, \mathbf{a})/partial a\) is linear in \(\mathbf{a}\) and so this is a linear optimisation problem. The Burg method is a constrained minimisation of \(E(Y, \mathbf{a})\) using the Levinson recursion, a computational device derived from the Yule-Walker method.

## Misc

Gradient descent learns Linear Dynamical systems

### Linear Predictive Coding

LPC introductions traditionally start with a physical model of the human vocal tract as a resonating pipe, then mumble away the details. This confused the hell out of me. AFAICT, an LPC model is just a list of AR regression coefficients and a driving noise source coefficient. This is “coding” because you can round the numbers, pack them down a smidgen and then use it to encode certain time series, such as the human voice, compactly. But it’s still a regression analysis, and can be treated as such.

The twists are that

- we usually think about it in a compression context
- Traditionally one performs many regressions to get time-varying models

It’s commonly described as a physical model because
we can imagine these regression coefficients corresponding
to a simplified physical model of the human vocal tract;
But we can think of the regression coefficients as corresponding to *any*
all-pole linear system, so I don’t think that brings special insight;
especially as the models of, say, a resonating pipe,
would intuitively be described by time-delays corresponding
to the *length* of the pipe,
not time-lags corresponding to a corresponding sample
plus computational convenience.
Sure we can get *similar* spectral response for this model as with a pipe,
according to linear systems theory,
but if you are going to assume so much advanced linear systems theory anyway,
and mix it with crappy physics,
why not just start with the linear systems and ditch the physics?

To discuss: these coefficients as spectrogram smoothing.

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