Filtering Problem (Stochastic Processes) (Filtering Problem (Stochastic Processes))

Description

The filtering problem is to obtain the best linear estimate $\hat{z}_t$ of $z_t$ based on the past observations ($y_s | s\leq t)$. Abstractly, the solution to the problem of filtering corresponds to explicitly computing

$\hat{z}_t = P_t^y(z_t)$

where $P_t^y$ is the projection operator onto the Hilbert space $H_t^y$.

Parameters

$n$: number of dimensions in state space

Table of Algorithms

Name	Year	Time	Space	Approximation Factor	Model	Reference
Kalman Filter	1960	$O(n^{3})$	$O(n^{2})$?	Exact	Deterministic
Particle filter Del Moral	1996	$O(n^{3})$	$O(nN)$?	Exact	Deterministic
Stratonovich	1959	$O(n^{3})$		Exact	Deterministic
Stratonovich	1960	$O(n^{3})$		Exact	Deterministic
Kushner non-linear filter	1967	$O(n^{3})$	$O(n^{2})$??	Exact	Deterministic	Time
Zakai	1969	$O(n^{3})$		Exact	Deterministic	Time
Maybeck; Peter S Extended Kalman Filter	1979	$O(n^{2} \log^{2} n)$	$O(n^{2})$?	Exact	Deterministic
Damiano Brigo; Bernard Hanzon and François LeGland	1998	$O(n^{2.{4}5} \log n)$		Exact	Deterministic	Time
Del Moral; Pierre	1998	$O(n^{2})$		Exact	Deterministic	Time
Ocone	1999	$O(n^{2})$		Exact	Deterministic	Time

Time Complexity Graph