
ScoreBased Parameter Estimation for a Class of ContinuousTime State Space Models
We consider the problem of parameter estimation for a class of continuou...
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Parameter Estimation for the McKeanVlasov Stochastic Differential Equation
In this paper, we consider the problem of parameter estimation for a sto...
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Unbiased Estimation of the Gradient of the LogLikelihood in Inverse Problems
We consider the problem of estimating a parameter associated to a Bayesi...
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Shallow Neural Hawkes: Nonparametric kernel estimation for Hawkes processes
Multidimensional Hawkes process (MHP) is a class of self and mutually e...
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Stochastic Bouncy Particle Sampler
We introduce a novel stochastic version of the nonreversible, rejection...
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CTBNCToolkit: Continuous Time Bayesian Network Classifier Toolkit
Continuous time Bayesian network classifiers are designed for temporal c...
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Marginal Weighted Maximum Loglikelihood for Efficient Learning of PerturbandMap models
We consider the structuredoutput prediction problem through probabilist...
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Unbiased Estimation of the Gradient of the LogLikelihood for a Class of ContinuousTime StateSpace Models
In this paper, we consider static parameter estimation for a class of continuoustime statespace models. Our goal is to obtain an unbiased estimate of the gradient of the loglikelihood (score function), which is an estimate that is unbiased even if the stochastic processes involved in the model must be discretized in time. To achieve this goal, we apply a doubly randomized scheme, that involves a novel coupled conditional particle filter (CCPF) on the second level of randomization. Our novel estimate helps facilitate the application of gradientbased estimation algorithms, such as stochasticgradient Langevin descent. We illustrate our methodology in the context of stochastic gradient descent (SGD) in several numerical examples and compare with the Rhee Glynn estimator.
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