95,053 research outputs found
STROOPWAFEL: Simulating rare outcomes from astrophysical populations, with application to gravitational-wave sources
Gravitational-wave observations of double compact object (DCO) mergers are
providing new insights into the physics of massive stars and the evolution of
binary systems. Making the most of expected near-future observations for
understanding stellar physics will rely on comparisons with binary population
synthesis models. However, the vast majority of simulated binaries never
produce DCOs, which makes calculating such populations computationally
inefficient. We present an importance sampling algorithm, STROOPWAFEL, that
improves the computational efficiency of population studies of rare events, by
focusing the simulation around regions of the initial parameter space found to
produce outputs of interest. We implement the algorithm in the binary
population synthesis code COMPAS, and compare the efficiency of our
implementation to the standard method of Monte Carlo sampling from the birth
probability distributions. STROOPWAFEL finds 25-200 times more DCO
mergers than the standard sampling method with the same simulation size, and so
speeds up simulations by up to two orders of magnitude. Finding more DCO
mergers automatically maps the parameter space with far higher resolution than
when using the traditional sampling. This increase in efficiency also leads to
a decrease of a factor 3-10 in statistical sampling uncertainty for the
predictions from the simulations. This is particularly notable for the
distribution functions of observable quantities such as the black hole and
neutron star chirp mass distribution, including in the tails of the
distribution functions where predictions using standard sampling can be
dominated by sampling noise.Comment: Accepted. Data and scripts to reproduce main results is publicly
available. The code for the STROOPWAFEL algorithm will be made publicly
available. Early inquiries can be addressed to the lead autho
Split Sampling: Expectations, Normalisation and Rare Events
In this paper we develop a methodology that we call split sampling methods to
estimate high dimensional expectations and rare event probabilities. Split
sampling uses an auxiliary variable MCMC simulation and expresses the
expectation of interest as an integrated set of rare event probabilities. We
derive our estimator from a Rao-Blackwellised estimate of a marginal auxiliary
variable distribution. We illustrate our method with two applications. First,
we compute a shortest network path rare event probability and compare our
method to estimation to a cross entropy approach. Then, we compute a
normalisation constant of a high dimensional mixture of Gaussians and compare
our estimate to one based on nested sampling. We discuss the relationship
between our method and other alternatives such as the product of conditional
probability estimator and importance sampling. The methods developed here are
available in the R package: SplitSampling
Molecular modeling for physical property prediction
Multiscale modeling is becoming the standard approach for process study in a broader framework that promotes computer aided integrated product and process design. In addition to usual purity requirements, end products must meet new constraints in terms of environmental impact, safety of goods and people, specific properties. This chapter adresses the use of molecular modeling tools for the prediction of physical property usefull for chemical engineering practice
A Multi-Species Asymmetric Exclusion Model with an Impurity
A multi-species generalization of the Asymmetric Simple Exclusion Process
(ASEP) has been considered in the presence of a single impurity on a ring. The
model describes particles hopping in one direction with stochastic dynamics and
hard core exclusion condition. The ordinary particles hop forward with their
characteristic hopping rates and fast particles can overtake slow ones with a
relative rate. The impurity, which is the slowest particle in the ensemble of
particles on the ring, hops in the same direction of the ordinary particles
with its intrinsic hopping rate and can be overtaken by ordinary particles with
a rate which is not necessarily a relative rate. We will show that the phase
diagram of the model can be obtained exactly. It turns out that the phase
structure of the model depends on the density distribution function of the
ordinary particles on the ring so that it can have either four phases or only
one. The mean speed of impurity and also the total current of the ordinary
particles are explicitly calculated in each phase. Using Monte Carlo
simulation, the density profile of the ordinary particles is also obtained. The
simulation data confirm all of the analytical calculations.Comment: 20 pages,10 EPS figures; to appear in Physica
Cross-entropy optimisation of importance sampling parameters for statistical model checking
Statistical model checking avoids the exponential growth of states associated
with probabilistic model checking by estimating properties from multiple
executions of a system and by giving results within confidence bounds. Rare
properties are often very important but pose a particular challenge for
simulation-based approaches, hence a key objective under these circumstances is
to reduce the number and length of simulations necessary to produce a given
level of confidence. Importance sampling is a well-established technique that
achieves this, however to maintain the advantages of statistical model checking
it is necessary to find good importance sampling distributions without
considering the entire state space.
Motivated by the above, we present a simple algorithm that uses the notion of
cross-entropy to find the optimal parameters for an importance sampling
distribution. In contrast to previous work, our algorithm uses a low
dimensional vector of parameters to define this distribution and thus avoids
the often intractable explicit representation of a transition matrix. We show
that our parametrisation leads to a unique optimum and can produce many orders
of magnitude improvement in simulation efficiency. We demonstrate the efficacy
of our methodology by applying it to models from reliability engineering and
biochemistry.Comment: 16 pages, 8 figures, LNCS styl
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