Band 26, Heft 3

A crude Monte Carlo (MC) method allows to calculate integrals over a d -dimensional cube. As the number N of integration nodes becomes large, the rate of probable error of the MC method decreases as O⁢(1/N){O(1/\sqrt{N})}. The use of quasi-random points instead of random points in the MC algorithm converts it to the quasi-Monte Carlo (QMC) method. The asymptotic error estimate of QMC integration of d -dimensional functions contains a multiplier 1/N{1/N}. However, the multiplier (ln⁡N)d{(\ln N)^{d}} is also a part of the error estimate, which makes it virtually useless. We have proved that, in the general case, the QMC error estimate is not limited to the factor 1/N{1/N}. However, our numerical experiments show that using quasi-random points of Sobol sequences with N=2m{N=2^{m}} with natural m makes the integration error approximately proportional to 1/N{1/N}. In our numerical experiments, d≤15{d\leq 15}, and we used N≤240{N\leq 2^{40}} points generated by the SOBOLSEQ16384 code published in 2011. In this code, d≤214{d\leq 2^{14}} and N≤263{N\leq 2^{63}}.

This study deals with a narrow escape problem, a well-know difficult problem of evaluating the probability for a diffusing particle to reach a small part of a boundary far away from the starting position of the particle. A direct simulation of the diffusion trajectories would take an enormous computer simulation time. Instead, we use a different approach which drastically improves the efficiency of the diffusion trajectory tracking algorithm by introducing an artificial drift velocity directed to the target position. The method can be efficiently applied to solve narrow escape problems for domains of long extension in one direction which is the case in many practical problems in biology and chemistry. The algorithm is meshless both in space and time, and is well applied to solve high-dimensional problems in complicated domains. We present in this paper a detailed numerical analysis of the method for the case of a rectangular parallelepiped. Both stationary and transient diffusion problems are handled.

Monte Carlo (MC) simulation depends on pseudo-random numbers. The generation of these numbers is examined in connection with the Brownian motion. We present the low discrepancy sequence known as Halton sequence that generates different stochastic samples in an equally distributed form. This will increase the convergence and accuracy using the generated different samples in the Multilevel Monte Carlo method (MLMC). We compare algorithms by using a pseudo-random generator and a random generator depending on a Halton sequence. The computational cost for different stochastic differential equations increases in a standard MC technique. It will be highly reduced using a Halton sequence, especially in multiplicative stochastic differential equations.

This article develops a new estimator of the marginal likelihood that requires only a sample of the posterior distribution as the input from the analyst. This sample may come from any sampling scheme, such as Gibbs sampling or Metropolis–Hastings sampling. The presented approach can be implemented generically in almost any application of Bayesian modeling and significantly decreases the computational burdens associated with marginal likelihood estimation compared to existing techniques. The functionality of this method is demonstrated in the context of probit and logit regressions, on two mixtures of normals models, and also on a high-dimensional random intercept probit. Simulation results show that the simple approach presented here achieves excellent stability in low-dimensional models, and also clearly outperforms existing methods when the number of coefficients in the model increases.

It has been shown that when using a Monte Carlo algorithm to estimate the electrostatic free energy of a biomolecule in a solution, individual random walks can become entrapped in the geometry. We examine a proposed solution, using a sharp restart during the Walk-on-Subdomains step, in more detail. We show that the point at which this solution introduces significant bias is related to properties intrinsic to the molecule being examined. We also examine two potential methods of generating a sharp restart point and show that they both cause no significant bias in the examined molecules and increase the stability of the run times of the individual walks.

The work is devoted to three-dimensional modeling of fractal sets of points. Additional constraints in the form of probability density caused by the frequency of the generated points’ spatial distribution are considered. The suggested method for statistical simulation allows reproducing both the given probability distribution defining the spatial position of the generated points and the required fractal dimension. Performed numerical computations confirm the accuracy and efficiency of the proposed method for the considered test models.

This paper proposes a weighted log-rank test that maintains sensitivity to realistic alternatives of two survival curves, such as crossing curves, in the presence of heavy censoring. The new test incorporates a weight function that changes over the censoring level, increasing adaptivity and flexibility of the commonly used weighted log-rank tests. The new statistic is asymptotically normal under the null hypothesis that there is no difference in survival between two groups. The performances of the new test are evaluated via simulations under both proportional and non-proportional alternatives. We illustrate the new method with a real-world application.