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Found 9 papers, 2 papers with code
Data Consistency Approach to Model Validation
The contribution in this paper is a general criterion to evaluate the consistency of a set of statistical models with respect to observed data.
How consistent is my model with the data? Information-Theoretic Model Check
The choice of model class is fundamental in statistical learning and system identification, no matter whether the class is derived from physical principles or is a generic black-box.
Probabilistic learning of nonlinear dynamical systems using sequential Monte Carlo
We are concerned with the problem of learning probabilistic models of dynamical systems from measured data.
Learning of state-space models with highly informative observations: a tempered Sequential Monte Carlo solution
In particular, for learning of unknown parameters in nonlinear state-space models, methods based on the particle filter (a Monte Carlo method) have proven very useful.
A flexible state space model for learning nonlinear dynamical systems
We consider a nonlinear state-space model with the state transition and observation functions expressed as basis function expansions.
Computationally Efficient Bayesian Learning of Gaussian Process State Space Models
We present a procedure for efficient Bayesian learning in Gaussian process state space models, where the representation is formed by projecting the problem onto a set of approximate eigenfunctions derived from the prior covariance structure.
Sequential Monte Carlo Methods for System Identification
One of the key challenges in identifying nonlinear and possibly non-Gaussian state space models (SSMs) is the intractability of estimating the system state.
Marginalizing Gaussian Process Hyperparameters using Sequential Monte Carlo
Gaussian process regression is a popular method for non-parametric probabilistic modeling of functions.
Identification of jump Markov linear models using particle filters
Jump Markov linear models consists of a finite number of linear state space models and a discrete variable encoding the jumps (or switches) between the different linear models.
