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Penalized importance sampling for parameter estimation in stochastic differential equations
Chronic wasting disease Euler-Maruyama scheme Maximum likelihood estimation Partially observed discrete sparse data Penalized importance sampling Stochastic di
2013/6/14
We consider the problem of estimating parameters of stochastic differential equations with discrete-time observations that are either completely or partially observed. The transition density between t...
Metamodel-based importance sampling for structural reliability analysis
reliability analysis importance sampling metamodeling error kriging random fields active learning rare events
2011/6/16
Structural reliability methods aim at computing the probability of failure of systems with
respect to some prescribed performance functions. In modern engineering such functions
usually resort to ru...
Quantile estimation with adaptive importance sampling
Quantile estimation law of iterated logarithm adaptive im-portance sampling stochastic approximation Robbins–Monro
2010/3/11
We introduce new quantile estimators with adaptive importance
sampling. The adaptive estimators are based on weighted samples
that are neither independent nor identically distributed. Using a
new l...
Estimation of cosmological parameters using adaptive importance sampling
Estimation cosmological parameters adaptive importance sampling
2010/3/19
We present a Bayesian sampling algorithm called adaptive importance sampling or Population
Monte Carlo (PMC), whose computational workload is easily parallelizable and thus has the potential to consi...
Least Squares Importance Sampling for Monte Carlo Security Pricing
Monte Carlo Simulations Variance Reduction Techniques Importance Sampling Derivatives Pricing
2010/4/27
We describe a simple Importance Sampling strategy for Monte Carlo simulations based on a least
squares optimization procedure. With several numerical examples, we show that such Least Squares Importa...