Stochastic Modelling and Computation Importance Sampling Plays a Crucial Role for Quasi-monte Carlo Methods
نویسندگان
چکیده
8:30 – 8:50 Registration 8:50 – 9:00 Opening Remarks 9:00 – 9:30 Mike Hutchinson, Australian National University Locally adaptive gridding of elevation data 9:30 – 10:00 Malcolm Hudson, Macquarie University Block Fisher scoring optimization of penalized likelihoods in emission tomography 10:00 – 10:40 Alex Smola, NICTA Nonparametric tests for distributions 10:40 – 11:00 Morning Tea 11:00 – 11:40 Sergey Bakin, Suncorp Regression tree ensemble models 11:40 – 12:20 Berwin Turlach, University of Western Australia On homotopy algorithms in statistics 12:20 – 13:30 Lunch 13:30 – 14:00 Moshe Haviv, The Hebrew University of Jerusalem On singularly perturbed Markov chains 14:00 – 14:30 Kevin Burrage, University of Queensland Multiscale modelling of cellular processes 14:30 – 15:00 Tania Prvan, Macquarie University Thin film models in a stochastic setting 15:00 – 15:30 Steve Roberts, Australian National University An application of sparse grids to the estimation of probability density functions 15:30 – 16:00 Afternoon Tea 16:00 – 16:30 Frances Kuo, University of New South Wales Importance sampling plays a crucial role for quasi-Monte Carlo methods 16:30 – 17:00 Roger Sidje, University of Queensland A QR-based tridiagonalization algorithm for nonsymmetric matrices 17:00 – 17:30 Garry Newsam, DSTO Domain decomposition relations as a source of fast algorithms for evaluating integral operators
منابع مشابه
On pricing discrete barrier options using conditional expectation and importance sampling Monte Carlo
Estimators for the price of a discrete barrier option based on conditional expectation and importance sampling variance reduction techniques are given. There are erroneous formulas for the conditional expectation estimator published in the literature: we derive the correct expression for the estimator. We use a simulated annealing algorithm to estimate the optimal parameters of exponential twis...
متن کاملComputation of the endogenous mortgage rates with randomized quasi-Monte Carlo simulations
The problem of computing the mortgage rate implied by a prepayment and interest rate model is considered. A Monte Carlo algorithm that uses a correlated sampling approach is introduced to simulate the model. Numerical results are used to compare Monte Carlo and randomized quasi-Monte Carlo methods with a numerical PDE solution. A particular randomized quasi-Monte Carlo method, random-start scra...
متن کاملRobust Adaptive Importance Sampling for Normal Random Vectors
Adaptive Monte Carlo methods are very efficient techniques designed to tune simulation estimators on-line. In this work, we present an alternative to stochastic approximation to tune the optimal change of measure in the context of importance sampling for normal random vectors. Unlike stochastic approximation, which requires very fine tuning in practice, we propose to use sample average approxim...
متن کاملA Martingale Control Variate Method for Option Pricing with Stochastic Volatility
A generic control variate method is proposed to price options under stochastic volatility models by Monte Carlo simulations. This method provides a constructive way to select control variates which are martingales in order to reduce the variance of unbiased option price estimators. We apply a singular and regular perturbation analysis to characterize the variance reduced by martingale control v...
متن کاملEfficient simulated maximum likelihood with an application to online retailing
Simulated maximum likelihood estimates an analytically intractable likelihood function with an empirical average based on data simulated from a suitable importance sampling distribution. In order to use simulated maximum likelihood in an efficient way, the choice of the importance sampling distribution as well as the mechanism to generate the simulated data are crucial. In this paper we develop...
متن کامل