23,813 research outputs found
Variance Reduction Techniques in Monte Carlo Methods
Monte Carlo methods are simulation algorithms to estimate a numerical quantity in a statistical model of a real system. These algorithms are executed by computer programs. Variance reduction techniques (VRT) are needed, even though computer speed has been increasing dramatically, ever since the introduction of computers. This increased computer power has stimulated simulation analysts to develop ever more realistic models, so that the net result has not been faster execution of simulation experiments; e.g., some modern simulation models need hours or days for a single ’run’ (one replication of one scenario or combination of simulation input values). Moreover there are some simulation models that represent rare events which have extremely small probabilities of occurrence), so even modern computer would take ’for ever’ (centuries) to execute a single run - were it not that special VRT can reduce theses excessively long runtimes to practical magnitudes.common random numbers;antithetic random numbers;importance sampling;control variates;conditioning;stratied sampling;splitting;quasi Monte Carlo
SMCTC : sequential Monte Carlo in C++
Sequential Monte Carlo methods are a very general class of Monte Carlo methods for sampling from sequences of distributions. Simple examples of these algorithms are used very widely in the tracking and signal processing literature. Recent developments illustrate that these techniques have much more general applicability, and can be applied very effectively to statistical inference problems. Unfortunately, these methods are often perceived as being computationally expensive and difficult to implement. This article seeks to address both of these problems. A C++ template class library for the efficient and convenient implementation of very general Sequential Monte Carlo algorithms is presented. Two example applications are provided: a simple particle filter for illustrative purposes and a state-of-the-art algorithm for rare event estimation
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
Unbiased and Consistent Nested Sampling via Sequential Monte Carlo
We introduce a new class of sequential Monte Carlo methods called Nested
Sampling via Sequential Monte Carlo (NS-SMC), which reframes the Nested
Sampling method of Skilling (2006) in terms of sequential Monte Carlo
techniques. This new framework allows convergence results to be obtained in the
setting when Markov chain Monte Carlo (MCMC) is used to produce new samples. An
additional benefit is that marginal likelihood estimates are unbiased. In
contrast to NS, the analysis of NS-SMC does not require the (unrealistic)
assumption that the simulated samples be independent. As the original NS
algorithm is a special case of NS-SMC, this provides insights as to why NS
seems to produce accurate estimates despite a typical violation of its
assumptions. For applications of NS-SMC, we give advice on tuning MCMC kernels
in an automated manner via a preliminary pilot run, and present a new method
for appropriately choosing the number of MCMC repeats at each iteration.
Finally, a numerical study is conducted where the performance of NS-SMC and
temperature-annealed SMC is compared on several challenging and realistic
problems. MATLAB code for our experiments is made available at
https://github.com/LeahPrice/SMC-NS .Comment: 45 pages, some minor typographical errors fixed since last versio
SMCTC: Sequential Monte Carlo in C++
Sequential Monte Carlo methods are a very general class of Monte Carlo methods for sampling from sequences of distributions. Simple examples of these algorithms are used very widely in the tracking and signal processing literature. Recent developments illustrate that these techniques have much more general applicability, and can be applied very effectively to statistical inference problems. Unfortunately, these methods are often perceived as being computationally expensive and difficult to implement. This article seeks to address both of these problems. A C++ template class library for the efficient and convenient implementation of very general Sequential Monte Carlo algorithms is presented. Two example applications are provided: a simple particle filter for illustrative purposes and a state-of-the-art algorithm for rare event estimation.
Forward Flux Sampling for rare event simulations
Rare events are ubiquitous in many different fields, yet they are notoriously
difficult to simulate because few, if any, events are observed in a conventiona
l simulation run. Over the past several decades, specialised simulation methods
have been developed to overcome this problem. We review one recently-developed
class of such methods, known as Forward Flux Sampling. Forward Flux Sampling
uses a series of interfaces between the initial and final states to calculate
rate constants and generate transition paths, for rare events in equilibrium or
nonequilibrium systems with stochastic dynamics. This review draws together a
number of recent advances, summarizes several applications of the method and
highlights challenges that remain to be overcome.Comment: minor typos in the manuscript. J.Phys.:Condensed Matter (accepted for
publication
Importance sampling large deviations in nonequilibrium steady states. I
Large deviation functions contain information on the stability and response
of systems driven into nonequilibrium steady states, and in such a way are
similar to free energies for systems at equilibrium. As with equilibrium free
energies, evaluating large deviation functions numerically for all but the
simplest systems is difficult, because by construction they depend on
exponentially rare events. In this first paper of a series, we evaluate
different trajectory-based sampling methods capable of computing large
deviation functions of time integrated observables within nonequilibrium steady
states. We illustrate some convergence criteria and best practices using a
number of different models, including a biased Brownian walker, a driven
lattice gas, and a model of self-assembly. We show how two popular methods for
sampling trajectory ensembles, transition path sampling and diffusion Monte
Carlo, suffer from exponentially diverging correlations in trajectory space as
a function of the bias parameter when estimating large deviation functions.
Improving the efficiencies of these algorithms requires introducing guiding
functions for the trajectories.Comment: Published in JC
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