We build on auto-encoding sequential Monte Carlo (aesmc):1 a method for model and proposal learning based on maximizing the lower bound to the log marginal likelihood in a broad family of structured probabilistic models. Our approach relies on the efficiency of sequential Monte Carlo (smc) for performing inference in structured probabilistic models and the flexibility of deep neural networks to...

Telecommunication networks, oil platforms, chemical plants and airplanes consist of a great number of subsystems and components that are all subject to failure. Reliability theory studies the failure behavior of such systems in relation to the failure behavior of their components, which is often easier to analyze. However, even for the most basic reliability models, the overall reliability of t...

The multilevel Monte Carlo and the control-variate method are reviewed for simulation of Coulomb collisions. The test case considered is fast-ion relaxation from ion-ion and electron-ion collisions against a Maxwellian background. A simplified energy scattering model is derived and related to the Cox-Ingersoll-Ross model for which, an exact time dependent solution can be obtained. The exact mod...

We build on auto-encoding sequential Monte Carlo (aesmc):1 a method for model and proposal learning based on maximizing the lower bound to the log marginal likelihood in a broad family of structured probabilistic models. Our approach relies on the efficiency of sequential Monte Carlo (smc) for performing inference in structured probabilistic models and the flexibility of deep neural networks to...

Solvent-free coarse grained models represent one of the most promising approaches for molecular simulations of mesoscopically large membranes. In these models, the size of the simulated membrane is limited by the slow relaxation time of the longest bending mode. Here, we present a Monte Carlo algorithm with update moves in which all the lipids are simultaneously displaced. These collective move...

We build on auto-encoding sequential Monte Carlo (aesmc):1 a method for model and proposal learning based on maximizing the lower bound to the log marginal likelihood in a broad family of structured probabilistic models. Our approach relies on the efficiency of sequential Monte Carlo (smc) for performing inference in structured probabilistic models and the flexibility of deep neural networks to...

The lack of a primary method for determination of optical parameters remains a significant barrier in optical study of turbid media. We present a complete system of experimental setups and Monte Carlo modeling tools for fast and accurate solution of the inverse problem from the measured signals of homogeneous turbid samples. The calibration of the instrument and validation of the Monte Carlo mo...

Accelerators such as Graphics Processing Units (GPUs) and Many Integrated Core (MIC) coprocessors are advanced computing devices with outstandingly high computing performance and energy efficiency. The Monte Carlo transport simulation community views these advanced devices as an opportunity to effectively reduce the computation time for performance-critical applications. In this paper, we repor...

Valuation and Damages for Franchised Businesses by Patrick L. Anderson A large number of businesses operate as “franchises,” in which a separate entity creates a brand identity for a product or service that is sold through a system of franchised retailers or wholesalers. Examples include automobile dealers, hotels, beer distributors, restaurants, video, auto and other rental outlets. These busi...