Kalman filtering (and filtering in general) considers the following setting: we have a sequence of states , which evolves under random perturbations over time. Unfortunately we cannot observe , we can only observe some noisy function of , namely, . Our task is to find the best estimate of given our observations of . Continue reading “Kalman Filter”
We briefly describe an Online Bayesian Framework which is sometimes referred to as Assumed Density Filer (ADF). And we review a heuristic proof of its convergence in the Gaussian case.
We consider the following formulation of Lai, Robbins and Wei (1979), and Lai and Wei (1982). Consider the following regression problem,
for where are unobservable random errors and are unknown parameters.
Typically for a regression problem, it is assumed that inputs are given and errors are IID random variables. However, we now want to consider a setting where we sequentially choose inputs and then get observations , and errors are a martingale difference sequence with respect to the filtration generated by .
We review the proof by Robbins and Munro for finding fixed points. Stochastic gradient descent, Q-learning and a bunch of other stochastic algorithms can be seen as variants of this basic algorithm. We review the basic ingredients of the original proof.
The Simplex Theorem suggests a method for solving linear programs . It is called the Simplex Algorithm.
A linear program is a constrained optimization problem with a linear objective, , and linear functional constraints, , we could for instance write
- Weighted majority algorithm its variant for Bandit Problems.