We study the problem of evaluating a policy that is different from the one that generates data. Such a problem, known as off-policy evaluation in reinforcement learning (RL), is encountered whenever one wants to estimate the value of a new solution, based on historical data, before actually deploying it in the real system, which is a critical step of applying RL in most real-world applications....

Let (X(t)) be a risk process with reserve-dependent premium rate, delayed claims and initial capital u. Consider a class of risk processes {(Xε(t)) : ε > 0} derived from (X(t)) via scaling in a slow Markov walk sense, and let Ψε(u) be the corresponding ruin probability. In this paper we prove sample path large deviations for (X(t)) as ε → 0. As a consequence, we give exact asymptotics for log Ψ...

The application of particle filters in geophysical systems is reviewed. Some background on Bayesian filtering is provided, and the existing methods are discussed. The emphasis is on the methodology, and not so much on the applications themselves. It is shown that direct application of the basic particle filter (i.e., importance sampling using the prior as the importance density) does not work i...

The valuation of basket default swaps depends crucially on the joint default probability of the underlying assets in the basket. It is known that this probability can be modeled by means of a copula function which links the marginal default probabilities to a joint probability. The valuation bears risk due to the uncertainty of the copula, the relation of the assets to each other and the margin...

M Carlo simulation is widely used to measure the credit risk in portfolios of loans, corporate bonds, and other instruments subject to possible default. The accurate measurement of credit risk is often a rare-event simulation problem because default probabilities are low for highly rated obligors and because risk management is particularly concerned with rare but significant losses resulting fr...

Recent studies have shown that power systems protection mechanisms have played a major role in propagating disturbances. Out of the last five major Western Systems Coordinating Council (WSCC) events (the North Ridge earthquake, December 14 1994, July 2 & 3 1996, and August 1

Full likelihood inference under Kingman’s coalescent is a computationally challenging problem to which importance sampling (IS) and the product of approximate conditionals (PAC) method have been applied successfully. Both methods can be expressed in terms of families of intractable conditional sampling distributions (CSDs), and rely on principled approximations for accurate inference. Recently,...

An appearance-based EigenTracker can track objects which simultaneously undergo image motions as well as changes in view. This paper enhances the framework in two ways. First, we incorporate a novel CONDENSATION-based predictive framework to speed up the EigenTracker. Next, our scheme is on-line: we use efficient eigenspace updates to track unknown objects. We use Importance Sampling for enhanc...