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278
Bayesian parameter inference for stochastic biochemical network models using particle mcmc
 Interface Focus
, 2011
"... Computational systems biology is concerned with the development of detailed mechanistic models of biological processes. Such models are often stochastic and analytically intractable, containing uncertain parameters which must be estimated from time course data. Inference for the parameters of comple ..."
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Cited by 56 (7 self)
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Computational systems biology is concerned with the development of detailed mechanistic models of biological processes. Such models are often stochastic and analytically intractable, containing uncertain parameters which must be estimated from time course data. Inference for the parameters of complex nonlinear multivariate stochastic process models is a challenging problem, but algorithms based on particle MCMC turn out to be a very effective computationally intensive approach to the problem. 1
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.
Macroeconomic Dynamics Near The ZLB: A Tale Of Two Equilibria,” manuscript
, 2012
"... This paper studies the dynamics of a New Keynesian DSGE model near the zero lower bound (ZLB) on nominal interest rates. In addition to the standard targetedinflation equilibrium, we consider a deflation equilibrium as well as a Markov sunspot equilibrium that switches between a targetedinflation a ..."
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Cited by 31 (4 self)
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This paper studies the dynamics of a New Keynesian DSGE model near the zero lower bound (ZLB) on nominal interest rates. In addition to the standard targetedinflation equilibrium, we consider a deflation equilibrium as well as a Markov sunspot equilibrium that switches between a targetedinflation and a deflation regime. We use the particle filter to estimate the state of the U.S. economy during and after the 200809 recession under the assumptions that the U.S. economy has been in either the targetedinflation or the sunspot equilibrium. We consider a combination of fiscal policy (calibrated to the American Recovery and Reinvestment Act) and monetary policy (that tries to keep interest rates near zero) and compute government spending multipliers. Exante multipliers (cumulative over one year) under the targetedinflation regime are around 0.9. A monetary policy that keeps interest rates at zero can raise the multiplier to 1.7. The expost (conditioning on the realized shocks in 20092011) multiplier is estimated to be 1.3. Conditional on the sunspot equilibrium the multipliers are generally smaller and the scope for conventional expansionary monetary policy is
Bayesian nonparametric inference of switching linear dynamical systems
, 2010
"... Abstract—Many complex dynamical phenomena can be effectively modeled by a system that switches among a set of conditionally linear dynamical modes. We consider two such models: the switching linear dynamical system (SLDS) and the switching vector autoregressive (VAR) process. Our Bayesian nonparamet ..."
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Cited by 23 (4 self)
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Abstract—Many complex dynamical phenomena can be effectively modeled by a system that switches among a set of conditionally linear dynamical modes. We consider two such models: the switching linear dynamical system (SLDS) and the switching vector autoregressive (VAR) process. Our Bayesian nonparametric approach utilizes a hierarchical Dirichlet process prior to learn an unknown number of persistent, smooth dynamical modes. We additionally employ automatic relevance determination to infer a sparse set of dynamic dependencies allowing us to learn SLDS with varying state dimension or switching VAR processes with varying autoregressive order. We develop a sampling algorithm that combines a truncated approximation to the Dirichlet process with efficient joint sampling of the mode and state sequences. The utility and flexibility of our model are demonstrated on synthetic data, sequences of dancing honey bees, the IBOVESPA stock index and a maneuvering target tracking application. Index Terms—Autoregressive processes, Bayesian methods, hidden Markov models, statespace methods, time series analysis,
Convergence properties of pseudomarginal Markov Chain Monte Carlo algorithms. Annals of Applied Probability
, 2012
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Bayesian Parameter Estimation for Latent Markov Random Fields and Social Networks
 Journal of Computational and Graphical Statistics
, 2012
"... Markov random fields and social networks ABACDEF ..."
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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 15 (8 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
Efficient Bayesian Inference for Switching StateSpace Models using Particle Markov Chain Monte Carlo Methods
, 2010
"... Switching statespace models (SSSM) are a popular class of time series models that have found many applications in statistics, econometrics and advanced signal processing. Bayesian inference for these models typically relies on Markov chain Monte Carlo (MCMC) techniques. However, even sophisticated ..."
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Cited by 14 (1 self)
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Switching statespace models (SSSM) are a popular class of time series models that have found many applications in statistics, econometrics and advanced signal processing. Bayesian inference for these models typically relies on Markov chain Monte Carlo (MCMC) techniques. However, even sophisticated MCMC methods dedicated to SSSM can prove quite inefficient as they update potentially strongly correlated variables oneatatime. Particle Markov chain Monte Carlo (PMCMC) methods are a recently developed class of MCMC algorithms which use particle filters to build efficient proposal distributions in highdimensions [1]. The existing PMCMC methods of [1] are applicable to SSSM, but are restricted to employing standard particle filtering techniques. Yet, in the context of SSSM, much more efficient particle techniques have been developed [22, 23, 24]. In this paper, we extend the PMCMC framework to enable the use of these efficient particle methods within MCMC. We demonstrate the resulting generic methodology on a variety of examples including a multiple changepoints model for welllog data and a model for U.S./U.K. exchange rate data. These new PMCMC algorithms are shown to outperform experimentally stateoftheart MCMC techniques for a fixed computational complexity. Additionally they can be easily parallelized [39] which allows further substantial gains.
Iterated Filtering
, 2011
"... Inference for partially observed Markov process models has been a longstanding methodological challenge with many scientific and engineering applications. Iterated filtering algorithms maximize the likelihood function for partially observed Markov process models by solving a recursive sequence of fi ..."
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Cited by 14 (3 self)
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Inference for partially observed Markov process models has been a longstanding methodological challenge with many scientific and engineering applications. Iterated filtering algorithms maximize the likelihood function for partially observed Markov process models by solving a recursive sequence of filtering problems. We present new theoretical results pertaining to the convergence of iterated filtering algorithms implemented via sequential Monte Carlo filters. This theory complements the growing body of empirical evidence that iterated filtering algorithms provide an effective inference strategy for scientific models of nonlinear dynamic systems. The first step in our theory involves studying a new recursive approach for maximizing the likelihood function of a latent variable model, when this likelihood is evaluated via importance sampling. This leads to the consideration of an iterated importance sampling algorithm which serves as a simple special case of iterated filtering, and may have applicability in its own right. 1