Probability – Page 4 – Applied Probability Notes

Markov Chains

A short introduction to Markov chains for dynamic programming
Definition, Markov Property, some Potential Theory.

Talagrand’s Concentration Inequality

We prove a powerful inequality which provides very tight gaussian tail bounds “ $e^{-ct^2}$ ” for probabilities on product state spaces $\Omega^n$ . Talagrand’s Inequality has found lots of applications in probability and combinatorial optimization and, if one can apply it, it generally outperforms inequalities like Azzuma-Hoeffding.

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Markov Chains: a functional view

Laplacian; Adjoints; Harmonic fn; Green’s fn; Forward Eqn; Backward Eqn.
Markov Chains and Martingales; Green’s Functions and occupancy; Potential functions; time-reversal and adjoints.

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Spitzer’s Lyapunov Ergodicity

We show that relative entropy decreases for continuous time Markov chains.

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A Mean Field Limit

We consider a system consisting of $N$ interacting objects. As we let the number of objects increase, we can characterize the limiting behaviour of the system.

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Cross Entropy Method

In the Cross Entropy Method, we wish to estimate the likelihood

$l = {\mathbb P} ( S(X) \geq \gamma ).$

Here $X$ is a random variable whose distribution is known and belongs to a parametrized family of densities $f( , v)$ . Further $S(X)$ is often a solution to an optimization problem.

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Sanov’s Theorem

Sanov’s asks how likely is it that the empirical distribution some IIDRV’s is far from the distribution. And shows that the relative entropy determines the likelihood of being far.

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