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## PowerPoint Slideshow about ' State Space Models' - kim-johns

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### Kalman Filtering Markov Model satisfies:

### State Space Models Markov Model satisfies:

Let { xt:t T} and { yt:t T} denote two vector valued time series that satisfy the system of equations:

yt = Atxt+ vt (The observation equation)

xt = Btxt-1+ ut (The state equation)

The time series { yt:t T} is said to have state-space representation.

Note: { ut:t T} and { vt:t T} denote two vector valued time series that satisfying:

- E(ut) = E(vt) = 0.
- E(utusˊ) = E(vtvsˊ) = 0 if t ≠ s.
- E(ututˊ) = Suand E(vtvtˊ) = Sv.
- E(utvsˊ) = E(vtusˊ) = 0 for all t and s.

Example: One might be tracking an object with several radar stations. The process {xt:t T} gives the position of the object at time t. The process { yt:t T} denotes the observations at time t made by the several radar stations.

As in the Hidden Markov Model we will be interested in determining position of the object, {xt:t T}, from the observations, {yt:t T} , made by the several radar stations

Example: Many of the models we have considered to date can be thought of a State-Space models

Autoregressive model of order p:

Hidden Markov Model: Assume that there are m states. Also that there the observations Yt are discreet and take on n possible values.

Suppose that the m states are denoted by the vectors:

Suppose that the n possible observations taken at each state are

Hence

and

where diag(v) = the diagonal matrix with the components of the vector v along the diagonal

Hence with these definitions the state sequence of a Hidden Markov Model satisfies:

The State Equation

with

and

The observation sequence satisfies:

The Observation Equation

with

and

We are now interested in determining the state vector Markov Model satisfies:xt in terms of some or all of the observation vectors y1, y2, y3, … , yT.

We will consider finding the “best” linear predictor.

We can include a constant term if in addition one of the observations (y0 say) is the vector of 1’s.

We will consider estimation of xt in terms of

- y1, y2, y3, … , yt-1(the prediction problem)
- y1, y2, y3, … , yt (the filtering problem)
- y1, y2, y3, … , yT (t < T, the smoothing problem)

For any vector Markov Model satisfies:x define:

where

is the best linear predictor of x(i), the ith component of x, based on y0, y1, y2, … , ys.

The best linear predictor of x(i) is the linear function that of x, based on y0, y1, y2, … , ys that minimizes

Remark Markov Model satisfies:: The best predictor is the unique vector of the form:

Where C0, C1, C2, … ,Cs, are selected so that:

Remark Markov Model satisfies:: If x, y1, y2, … ,ys are normally distributed then:

Remark Markov Model satisfies:

Let u and v, be two random vectors than

is the optimal linear predictor of u based on v if

Let { Markov Model satisfies: xt:t T} and { yt:t T} denote two vector valued time series that satisfy the system of equations:

yt = Atxt+ vt (The observation equation)

xt = Btxt-1+ ut (The state equation)

The time series { yt:t T} is said to have state-space representation.

Note: Markov Model satisfies: { ut:t T} and { vt:t T} denote two vector valued time series that satisfying:

- E(ut) = E(vt) = 0.
- E(utusˊ) = E(vtvsˊ) = 0 if t ≠ s.
- E(ututˊ) = Suand E(vtvtˊ) = Sv.
- E(utvsˊ) = E(vtusˊ) = 0 for all t and s.

Kalman Filtering: Markov Model satisfies:

Let { xt:t T} and { yt:t T} denote two vector valued time series that satisfy the system of equations:

yt = Atxt+ vt

xt = Bxt-1+ ut

Let

and

Then Markov Model satisfies:

where

One also assumes that the initial vector x0 has mean mand covariance matrix S an that

The covariance matrices are updated Markov Model satisfies:

with

Let Markov Model satisfies:

Let

Given y0, y1, y2, … , yt-1 the best linear predictor of dt using et is:

Example: Markov Model satisfies:

Suppose we have an AR(2) time series

What is observe is the time series

{ut|t T} and {vt|t T} are white noise time series with standard deviations suand sv.

This model can be expressed as a state-space model by defining:

then

The Kalman equations defining:

1.

2. defining:

3. defining:

4. defining:

5. defining:

Kalman Filtering (smoothing): defining:

Now consider finding

These can be found by successive backward recursions for t = T, T – 1, … , 2, 1

where

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