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On the utility of graphics cards to perform massively parallel simulation of advanced Monte Carlo methods
 Journal of Computational and Graphical Statistics
, 2010
"... We present a casestudy on the utility of graphics cards to perform massively parallel simulation of advanced Monte Carlo methods. Graphics cards, containing multiple Graphics Processing Units (GPUs), are selfcontained parallel computational devices that can be housed in conventional desktop and la ..."
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Cited by 73 (11 self)
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We present a casestudy on the utility of graphics cards to perform massively parallel simulation of advanced Monte Carlo methods. Graphics cards, containing multiple Graphics Processing Units (GPUs), are selfcontained parallel computational devices that can be housed in conventional desktop and laptop computers. For certain classes of Monte Carlo algorithms they offer massively parallel simulation, with the added advantage over conventional distributed multicore processors that they are cheap, easily accessible, easy to maintain, easy to code, dedicated local devices with low power consumption. On a canonical set of stochastic simulation examples including populationbased Markov chain Monte Carlo methods and Sequential Monte Carlo methods, we find speedups from 35 to 500 fold over conventional singlethreaded computer code. Our findings suggest that GPUs have the potential to facilitate the growth of statistical modelling into complex data rich domains through the availability of cheap and accessible manycore computation. We believe the speedup we observe should motivate wider
A survey of sequential Monte Carlo methods for economics and finance
, 2009
"... This paper serves as an introduction and survey for economists to the field of sequential Monte Carlo methods which are also known as particle filters. Sequential Monte Carlo methods are simulation based algorithms used to compute the highdimensional and/or complex integrals that arise regularly in ..."
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Cited by 34 (7 self)
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This paper serves as an introduction and survey for economists to the field of sequential Monte Carlo methods which are also known as particle filters. Sequential Monte Carlo methods are simulation based algorithms used to compute the highdimensional and/or complex integrals that arise regularly in applied work. These methods are becoming increasingly popular in economics and finance; from dynamic stochastic general equilibrium models in macroeconomics to option pricing. The objective of this paper is to explain the basics of the methodology, provide references to the literature, and cover some of the theoretical results that justify the methods in practice.
Unfreezing the Robot: Navigation in Dense, Interacting Crowds
"... Abstract — In this paper, we study the safe navigation of a mobile robot through crowds of dynamic agents with uncertain trajectories. Existing algorithms suffer from the “freezing robot ” problem: once the environment surpasses a certain level of complexity, the planner decides that all forward pat ..."
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Cited by 29 (0 self)
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Abstract — In this paper, we study the safe navigation of a mobile robot through crowds of dynamic agents with uncertain trajectories. Existing algorithms suffer from the “freezing robot ” problem: once the environment surpasses a certain level of complexity, the planner decides that all forward paths are unsafe, and the robot freezes in place (or performs unnecessary maneuvers) to avoid collisions. Since a feasible path typically exists, this behavior is suboptimal. Existing approaches have focused on reducing the predictive uncertainty for individual agents by employing more informed models or heuristically limiting the predictive covariance to prevent this overcautious behavior. In this work, we demonstrate that both the individual prediction and the predictive uncertainty have little to do with the frozen robot problem. Our key insight is that dynamic agents solve the frozen robot problem by engaging in “joint collision avoidance”: They cooperatively make room to create feasible trajectories. We develop IGP, a nonparametric statistical model based on dependent output Gaussian processes that can estimate crowd interaction from data. Our model naturally captures the nonMarkov nature of agent trajectories, as well as their goaldriven navigation. We then show how planning in this model can be efficiently implemented using particle based inference. Lastly, we evaluate our model on a dataset of pedestrians entering and leaving a building, first comparing the model with actual pedestrians, and find that the algorithm either outperforms human pedestrians or performs very similarly to the pedestrians. We also present an experiment where a covariance reduction method results in highly overcautious behavior, while our model performs desirably. I.
Ancestor Sampling for Particle Gibbs
"... We present a novel method in the family of particle MCMC methods that we refer to as particle Gibbs with ancestor sampling (PGAS). Similarly to the existing PG with backward simulation (PGBS) procedure, we use backward sampling to (considerably) improve the mixing of the PG kernel. Instead of usin ..."
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Cited by 14 (7 self)
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We present a novel method in the family of particle MCMC methods that we refer to as particle Gibbs with ancestor sampling (PGAS). Similarly to the existing PG with backward simulation (PGBS) procedure, we use backward sampling to (considerably) improve the mixing of the PG kernel. Instead of using separate forward and backward sweeps as in PGBS, however, we achieve the same effect in a single forward sweep. We apply the PGAS framework to the challenging class of nonMarkovian statespace models. We develop a truncation strategy of these models that is applicable in principle to any backwardsimulationbased method, but which is particularly well suited to the PGAS framework. In particular, as we show in a simulation study, PGAS can yield an orderofmagnitude improved accuracy relative to PGBS due to its robustness to the truncation error. Several application examples are discussed, including RaoBlackwellized particle smoothing and inference in degenerate statespace models. 1
Phylogenetic inference via sequential Monte Carlo. Systematic Biology
, 2012
"... Abstract.—Bayesian inference provides an appealing general framework for phylogenetic analysis, able to incorporate a wide variety of modeling assumptions and to provide a coherent treatment of uncertainty. Existing computational approaches to Bayesian inference based on Markov Chain Monte Carlo (MC ..."
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Cited by 12 (2 self)
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Abstract.—Bayesian inference provides an appealing general framework for phylogenetic analysis, able to incorporate a wide variety of modeling assumptions and to provide a coherent treatment of uncertainty. Existing computational approaches to Bayesian inference based on Markov Chain Monte Carlo (MCMC) have not, however, kept pace with the scale of the data analysis problems in phylogenetics, and this has hindered the adoption of Bayesian methods. In this paper we present an alternative to MCMC based on Sequential Monte Carlo (SMC). We develop an extension of classical SMC based on partially ordered sets, and show how to apply this framework—which we refer to as PosetSMC—to phylogenetic analysis. We provide a theoretical treatment of PosetSMC and also present experimental evaluation of PosetSMC on both synthetic and real data. The empirical results demonstrate that PosetSMC is a very promising alternative to MCMC, providing up to two orders of magnitude faster convergence. We discuss other factors favorable to the adoption of PosetSMC in phylogenetics, including its ability to estimate marginal likelihoods, its ready implementability on parallel and distributed computing platforms, and the possibility of combining with MCMC in hybrid MCMCSMC schemes. Software for PosetSMC is available at
Bayesian semiparametric Wiener system identification
, 2013
"... We present a novel method for Wiener system identification. The method relies on a semiparametric, i.e. a mixed parametric/nonparametric, model of a Wiener system. We use a statespace model for the linear dynamical system and a nonparametric Gaussian process model for the static nonlinearity. We av ..."
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Cited by 10 (7 self)
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We present a novel method for Wiener system identification. The method relies on a semiparametric, i.e. a mixed parametric/nonparametric, model of a Wiener system. We use a statespace model for the linear dynamical system and a nonparametric Gaussian process model for the static nonlinearity. We avoid making strong assumptions, such as monotonicity, on the nonlinear mapping. Stochastic disturbances, entering both as measurement noise and as process noise, are handled in a systematic manner. The nonparametric nature of the Gaussian process allows us to handle a wide range of nonlinearities without making problemspecific parameterizations. We also consider sparsitypromoting priors, based on generalized hyperbolic distributions, to automatically infer the order of the underlying dynamical system. We derive an inference algorithm based on an efficient particle Markov chain Monte Carlo method, referred to as particle Gibbs with ancestor sampling. The method is profiled on two challenging identification problems with good results. Blind Wiener system identification is handled as a special case.
Ecological nonlinear state space model selection via adaptive particle markov chain monte carlo (adpmcmc
, 2010
"... ar ..."
Infinite Dynamic Bayesian Networks
"... We present the infinite dynamic Bayesian network model (iDBN), a nonparametric, factored statespace model that generalizes dynamic Bayesian networks (DBNs). The iDBN can infer every aspect of a DBN: the number of hidden factors, the number of values each factor can take, and (arbitrarily complex) c ..."
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Cited by 10 (0 self)
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We present the infinite dynamic Bayesian network model (iDBN), a nonparametric, factored statespace model that generalizes dynamic Bayesian networks (DBNs). The iDBN can infer every aspect of a DBN: the number of hidden factors, the number of values each factor can take, and (arbitrarily complex) connections and conditionals between factors and observations. In this way, the iDBN generalizes other nonparametric statespacemodels, whichuntilnowgenerally focused on binary hidden nodes and more restricted connection structures. We show how this new prior allows us to find interesting structureinbenchmarktestsandontworealworld datasets involving weather data and neural information flow networks. 1.
Sequential Bayesian Filtering in Ocean Acoustics
, 2010
"... Sequential filtering provides an optimal framework for estimating and updating the unknown parameters of a system as data become available. Despite significant progress in the general theory and implementation, sequential Bayesian filters have been sparsely applied to ocean acoustics. The foundatio ..."
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Cited by 9 (7 self)
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Sequential filtering provides an optimal framework for estimating and updating the unknown parameters of a system as data become available. Despite significant progress in the general theory and implementation, sequential Bayesian filters have been sparsely applied to ocean acoustics. The foundations of sequential Bayesian filtering with emphasis on practical issues are first presented covering both Kalman and particle filter approaches. Filtering becomes a powerful estimation tool, employing prediction from previous estimates and updates stemming from physical and statistical models that relate acoustic measurements to the unknown parameters. Ocean acoustic applications are then discussed focusing on the estimation of environmental parameters evolving in time or space. The potential of particle filtering in ocean acoustics is further demonstrated through application to experimental data from the Shallow Water 2006 experiment.