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08/17/2018
Data Consistency Approach to Model Validation
In scientific inference problems, the underlying statistical modeling as...
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06/25/2018
Learning dynamical systems with particle stochastic approximation EM
We present the particle stochastic approximation EM (PSAEM) algorithm fo...
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12/07/2017
How consistent is my model with the data? Information-Theoretic Model Check
The choice of model class is fundamental in statistical learning and sys...
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12/07/2017
Is My Model Flexible Enough? Information-Theoretic Model Check
The choice of model class is fundamental in statistical learning and sys...
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11/29/2017
Learning nonlinear state-space models using smooth particle-filter-based likelihood approximations
When classical particle filtering algorithms are used for maximum likeli...
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02/06/2017
Learning of state-space models with highly informative observations: a tempered Sequential Monte Carlo solution
Probabilistic (or Bayesian) modeling and learning offers interesting pos...
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03/17/2016
A flexible state space model for learning nonlinear dynamical systems
We consider a nonlinear state-space model with the state transition and ...
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06/07/2015
Computationally Efficient Bayesian Learning of Gaussian Process State Space Models
Gaussian processes allow for flexible specification of prior assumptions...
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03/20/2015
Sequential Monte Carlo Methods for System Identification
One of the key challenges in identifying nonlinear and possibly non-Gaus...
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02/06/2015
Marginalizing Gaussian Process Hyperparameters using Sequential Monte Carlo
Gaussian process regression is a popular method for non-parametric proba...
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09/25/2014
