Search Results for author:
Found 6 papers, 2 papers with code
RJHMC-Tree for Exploration of the Bayesian Decision Tree Posterior
This paper is directed towards learning decision trees from data using a Bayesian approach, which is challenging due to the potentially enormous parameter space required to span all tree models.
An Improved Random Matrix Prediction Model for Manoeuvring Extended Targets
This paper proposes an improved prediction update for extended target tracking with the random matrix model.
Beyond Occam's Razor in System Identification: Double-Descent when Modeling Dynamics
It is typically observed that the model validation performance follows a U-shaped curve as the model complexity increases.
Deep Energy-Based NARX Models
This paper is directed towards the problem of learning nonlinear ARX models based on system input--output data.
A Bayesian Filtering Algorithm for Gaussian Mixture Models
A Bayesian filtering algorithm is developed for a class of state-space systems that can be modelled via Gaussian mixtures.
On the construction of probabilistic Newton-type algorithms
It has recently been shown that many of the existing quasi-Newton algorithms can be formulated as learning algorithms, capable of learning local models of the cost functions.
