Zobrazeno 1 - 10
of 37
pro vyhledávání: '"Nagapetyan, Tigran"'
We study statistical model checking of continuous-time stochastic hybrid systems. The challenge in applying statistical model checking to these systems is that one cannot simulate such systems exactly. We employ the multilevel Monte Carlo method (MLM
Externí odkaz:
http://arxiv.org/abs/1706.08270
We analyse a multilevel Monte Carlo method for the approximation of distribution functions of univariate random variables. Since, by assumption, the target distribution is not known explicitly, approximations have to be used. We provide an asymptotic
Externí odkaz:
http://arxiv.org/abs/1706.06869
Autor:
Nagapetyan, Tigran, Duncan, Andrew B., Hasenclever, Leonard, Vollmer, Sebastian J., Szpruch, Lukasz, Zygalakis, Konstantinos
The problem of posterior inference is central to Bayesian statistics and a wealth of Markov Chain Monte Carlo (MCMC) methods have been proposed to obtain asymptotically correct samples from the posterior. As datasets in applications grow larger and l
Externí odkaz:
http://arxiv.org/abs/1706.02692
Autor:
Giles, Mike, Nagapetyan, Tigran, Szpruch, Lukasz, Vollmer, Sebastian, Zygalakis, Konstantinos
Markov chain Monte Carlo (MCMC) algorithms are ubiquitous in Bayesian computations. However, they need to access the full data set in order to evaluate the posterior density at every step of the algorithm. This results in a great computational burden
Externí odkaz:
http://arxiv.org/abs/1609.06144
As the size of engineered systems grows, problems in reliability theory can become computationally challenging, often due to the combinatorial growth in the cut sets. In this paper we demonstrate how Multilevel Monte Carlo (MLMC) - a simulation appro
Externí odkaz:
http://arxiv.org/abs/1609.00691
In this paper we present a novel approach towards variance reduction for discretised diffusion processes. The proposed approach involves specially constructed control variates and allows for a significant reduction in the variance for the terminal fu
Externí odkaz:
http://arxiv.org/abs/1510.03141
Autor:
Belomestny, Denis, Nagapetyan, Tigran
In this paper a novel modification of the multilevel Monte Carlo approach, allowing for further significant complexity reduction, is proposed. The idea of the modification is to use the method of control variates to reduce variance at level zero. We
Externí odkaz:
http://arxiv.org/abs/1412.4045
Autor:
Belomestny, Denis, Nagapetyan, Tigran
In this paper we discuss the possibility of using multilevel Monte Carlo (MLMC) methods for weak approximation schemes. It turns out that by means of a simple coupling between consecutive time discretisation levels, one can achieve the same complexit
Externí odkaz:
http://arxiv.org/abs/1406.2581
In this article we propose a novel approach to reduce the computational complexity of various approximation methods for pricing discrete time American options. Given a sequence of continuation values estimates corresponding to different levels of spa
Externí odkaz:
http://arxiv.org/abs/1303.1334
The famous Afriat's theorem from the theory of revealed preferences establishes necessary and suffient conditions for existence of utility function for a given set of choices and prices. The result on existence of a {\it homogeneous} utility function
Externí odkaz:
http://arxiv.org/abs/1302.4695