Related papers: Precise Monte Carlo Simulation of Single-Photon De…
Statisticians often use Monte Carlo methods to approximate probability distributions, primarily with Markov chain Monte Carlo and importance sampling. Sequential Monte Carlo samplers are a class of algorithms that combine both techniques to…
This work presents optical calculations and simulations for scintillation detectors used in precision measurements of beta-particle energy spectra. Particular attention is given to Cherenkov photons and the impact of the light detection…
We briefly review the principles, mathematical bases, numerical shortcuts and applications of fast random walk (FRW) algorithms. This Monte Carlo technique allows one to simulate individual trajectories of diffusing particles in order to…
The paper proposes a new Monte-Carlo simulator combining the advantages of Sequential Monte Carlo simulators and Hamiltonian Monte Carlo simulators. The result is a method that is robust to multimodality and complex shapes to use for…
Monte Carlo methods are widely used to estimate observables in many-body quantum systems. However, conventional sampling schemes often require a large number of samples to achieve sufficient accuracy. In this work we propose the…
We have recently introduced the idea of making use of three-photon positron annihilations in positron emission tomography. In this paper the basic characteristics of the three-gamma imaging in PET are studied by means of Monte Carlo…
Quasi-Monte Carlo methods have become the industry standard in computer graphics. For that purpose, efficient algorithms for low discrepancy sequences are discussed. In addition, numerical pitfalls encountered in practice are revealed. We…
We propose a new framework for how to use sequential Monte Carlo (SMC) algorithms for inference in probabilistic graphical models (PGM). Via a sequential decomposition of the PGM we find a sequence of auxiliary distributions defined on a…
We present a new Monte Carlo method for obtaining solutions of the Boltzmann equation for describing phonon transport in micro and nanoscale devices. The proposed method can resolve arbitrarily small signals (e.g. temperature differences)…
Si- and Ge-based single-photon-avalanche-diodes (SPAD) are investigated by using self-consistent 3D Monte Carlo simulation, in a mixed-mode approach including the presence of a passive quenching circuit. This approach of transport allows us…
Rate-dependent effects in the electronics used to instrument the tagger focal plane at the MAX IV Laboratory have been investigated using the novel approach of Monte Carlo simulation. Results are compared to analytical calculations as well…
Parameter estimation for discretely observed Markov processes is a challenging problem. However, simulation of Markov processes is straightforward using the Gillespie algorithm. We exploit this ease of simulation to develop an effective…
The results of a 1-D Monte-Carlo program, which simulates the avalanche multiplication in diodes with depths of the order of 1 $\mu$m based on the method proposed in Ref.1, are presented. The aim of the study is to achieve a deeper…
The aim of this paper is to introduce a new Monte Carlo method based on importance sampling techniques for the simulation of stochastic differential equations. The main idea is to combine random walk on squares or rectangles methods with…
Biasing or importance sampling is a powerful technique in Monte Carlo radiative transfer, and can be applied in different forms to increase the accuracy and efficiency of simulations. One of the drawbacks of the use of biasing is the…
Several physics methods for the simulation of the photoelectric effect are quantitatively evaluated with respect to a large collection of experimental data retrieved from the literature. They include theoretical and empirical calculations…
Population annealing Monte Carlo is an efficient sequential algorithm for simulating k-local Boolean Hamiltonians. Because of its structure, the algorithm is inherently parallel and therefore well suited for large-scale simulations of…
Sequential Monte Carlo methods have been a major breakthrough in the field of numerical signal processing for stochastic dynamical state-space systems with partial and noisy observations. However, these methods still present certain…
Hamiltonian Monte Carlo is a widely used algorithm for sampling from posterior distributions of complex Bayesian models. It can efficiently explore high-dimensional parameter spaces guided by simulated Hamiltonian flows. However, the…
Rate-dependent effects in the electronics used to instrument the tagger focal plane at the MAX IV Laboratory were recently investigated using the novel approach of Monte Carlo simulation to allow for normalization of high-rate experimental…