Serwis Infona wykorzystuje pliki cookies (ciasteczka). Są to wartości tekstowe, zapamiętywane przez przeglądarkę na urządzeniu użytkownika. Nasz serwis ma dostęp do tych wartości oraz wykorzystuje je do zapamiętania danych dotyczących użytkownika, takich jak np. ustawienia (typu widok ekranu, wybór języka interfejsu), zapamiętanie zalogowania. Korzystanie z serwisu Infona oznacza zgodę na zapis informacji i ich wykorzystanie dla celów korzytania z serwisu. Więcej informacji można znaleźć w Polityce prywatności oraz Regulaminie serwisu. Zamknięcie tego okienka potwierdza zapoznanie się z informacją o plikach cookies, akceptację polityki prywatności i regulaminu oraz sposobu wykorzystywania plików cookies w serwisie. Możesz zmienić ustawienia obsługi cookies w swojej przeglądarce.
Monte Carlo simulations are used to tackle a wide range of exciting and complex problems, such as option pricing and biophotonic modelling. Since Monte Carlo simulations are both computationally expensive and highly parallelizable, they are ideally suited for acceleration through GPUs and FPGAs. Alongside these accelerators, Multilevel Monte Carlo techniques can be harnessed to further hasten simulations...
In reinforcement learning, exploration is typically conducted by taking occasional random actions. The literature lacks an exploration method driven by uncertainty, in which exploratory actions explicitly seek to improve the learning process in a sequential decision problem. In this paper, we propose a framework called Divergence-to-Go, which is a model-based method that uses recursion similarly to...
Model scoring in latent factor models is essential for a broad spectrum of applications such as clustering, change point detection or model order estimation. In a Bayesian setting, model selection is achieved via computation of the marginal likelihood. However, this is a typically challenging task as it involves calculation of a multidimensional integral over all the latent variables. In this paper,...
Schruben (1983) developed standardized time series (STS) methods to construct confidence intervals (CIs) for the steady-state mean of a stationary process. STS techniques cancel out the variance constant in the asymptotic distribution of the centered and scaled estimator, thereby eliminating the need to consistently estimate the asymptotic variance to obtain a CI. This is desirable since estimating...
Quantum Monte Carlo (QMC) simulations for the recent studies on complex materials were confronted by new computational challenges. Traditional approach to accelerate the simulations by parallel Monte Carlo chains faces serious scalability problems since the speedup is reaching the limitation predicted by Amdahl's law. Fine-grained parallelization of matrix kernels is essential to achieve better performance...
Quantiles, which are known as values-at-risk in finance, are often used to measure risk. Confidence intervals provide a way of assessing the error of quantile estimators. When estimating extreme quantiles using crude Monte Carlo, the confidence intervals may have large half-widths, thus motivating the use of variance-reduction techniques (VRTs). This paper develops methods for constructing confidence...
Markov-Chain Monte-Carlo (MCMC) methods are an important class of simulation techniques, which execute a sequence of simulation steps, where each new step depends on the previous ones. Due to this fundamental dependency, MCMC methods are inherently hard to parallelize on any architecture. The upcoming generations of hybrid CPU/GPGPU architectures with their multi-core CPUs and tightly coupled many-core...
Model comparison for the purposes of selection, averaging and validation is a problem which is found throughout statistics and signal processing. Within the Bayesian paradigm, these problems all require the calculation of the posterior probabilities of models within a particular class. Substantial progress has been made in recent years, but there are numerous difficulties in the practical implementation...
This paper describes and explores applications of several new methods for explaining unexpected behavior in Monte Carlo simulations: (1) the use of fuzzy logic to represent the extent to which a program behaves as expected, (2) the analysis of variable value density distributions, and (3) the geometric treatment of predicate lists as vectors when comparing simulation runs with normal and unexpected...
The huge computing power available in some graphic cards may be used to significantly speedup scientific computing compared with common parallel clusters. The low price and virtually ubiquitous Graphics Processing Units (GPU), in conjunction with C style parallel programming tools, like CUDA (Compute Unified Device Architecture), allow the programmers to exploit their fine grain parallelism and multithreading...
In order to deal with the issues of implicit limit state function and high computational cost in the mechanism reliability analysis, the methodology for predicting the failure probability of mechanism by combining simulation technology and support vector machine (SVM) based reliability analysis is introduced. Then, two strategies of mechanism reliability analysis using support vector machine are proposed,...
Monte-Carlo (MC) simulation is an effective tool for solving complex problems such as many-body simulation, exotic option pricing and partial differential equation solving. The huge amount of computation in MC makes it a good candidate for acceleration using hardware and distributed computing platforms. We propose a novel MC simulation framework suitable for a wide range of problems. This framework...
We compare two methods with different accuracy in modeling of multiple scatter estimation in 3D PET. In the first approach, the single scatter is obtained from model-based simulation while the multiple scatter projections are estimated by convolving the single scatter projections with a one-dimensional Gaussian kernel. A new convolution kernel model is proposed, in which both the amplitude and width...
The architecture of the latest Graphic Processing Unit (GPU) consists of a number of uniform programmable units integrated on the same chip, which facilitate the general-purpose computing beyond the graphic processing. With the multiple programmable units executing in parallel, the latest GPU shows superior performance for many non-graphic applications. Furthermore, programmers can have a direct control...
Random number generation is the kernel of Monte Carlo method and simulation, and it's sometimes necessary to generate a random vector from an unknown distribution described by a group of weighted samples. Based on the idea of partial approximation, a novel Weighted-Sample-Based Random Vector Generation (WSB-RVG) algorithm is proposed in this paper, which skips the estimation of the unknown density...
The increasing availability of multi-core and multiprocessor architectures provides new opportunities for improving the performance of many computer simulations. Markov Chain Monte Carlo (MCMC) simulations are widely used for approximate counting problems, Bayesian inference and as a means for estimating very high-dimensional integrals. As such MCMC has had a wide variety of applications in fields...
We present a nonparametric Bayesian model for completing low-rank, positive semidefinite matrices. Given an N × N matrix with underlying rank r, and noisy measured values and missing values with a symmetric pattern, the proposed Bayesian hierarchical model nonparametrically uncovers the underlying rank from all positive semidefinite matrices, and completes the matrix by approximating the missing values...
Consider the problem of estimating the expectation of a non linear function of a conditional expectation. This function is allowed to be non-differentiable and discontinuous at a finite set of points to capture practical settings. We develop a nested simulation strategy to estimate this via simulation and identify bias and optimized mean square error allocation. We show that this mean square error...
We propose a novel nonparametric approach to ARMAX identification with missing data relying upon recent work on predictor estimation via Gaussian regression. The Bayesian setup allows one to compute explicitly an input-output marginal density where the model dependence has been integrated out. This turns out to be a key step in facilitating the imputation of missing variables. Thus, this approach...
Podaj zakres dat dla filtrowania wyświetlonych wyników. Możesz podać datę początkową, końcową lub obie daty. Daty możesz wpisać ręcznie lub wybrać za pomocą kalendarza.