Practical Bayesian System Identification using Hamiltonian Monte Carlo

11/09/2020
by   Johannes Hendriks, et al.
0

This paper addresses Bayesian system identification using a Markov Chain Monte Carlo approach. In particular, the Metroplis-Hastings algorithm with a Hamiltonian proposal - known as Hamiltonian Monte Carlo - is reviewed and adapted to linear and nonlinear system identification problems. The paper details how the Hamiltonian proposal can be arranged so that the associated Markov Chain performs well within the Metropolis-Hastings setting, which is a key practical challenge faced when using the latter approach for many system identification problems. This combination is demonstrated on several examples, ranging from simple linear to more complex nonlinear systems, on both simulated and real data.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
07/05/2021

Antithetic Riemannian Manifold And Quantum-Inspired Hamiltonian Monte Carlo

Markov Chain Monte Carlo inference of target posterior distributions in ...
research
04/18/2020

Bayesian Parameter Identification for Jump Markov Linear Systems

This paper presents a Bayesian method for identification of jump Markov ...
research
04/13/2022

Investigating the efficiency of marginalising over discrete parameters in Bayesian computations

Bayesian analysis methods often use some form of iterative simulation su...
research
05/25/2023

Fractional Polynomials Models as Special Cases of Bayesian Generalized Nonlinear Models

We propose a framework for fitting fractional polynomials models as spec...
research
11/25/2017

Generalizing Hamiltonian Monte Carlo with Neural Networks

We present a general-purpose method to train Markov chain Monte Carlo ke...
research
11/15/2021

A Two-Dimensional Intrinsic Gaussian Markov Random Field for Blood Pressure Data

Many real-world phenomena are naturally bivariate. This includes blood p...
research
11/21/2017

The joint projected normal and skew-normal: a distribution for poly-cylindrical data

The contribution of this work is the introduction of a multivariate circ...

Please sign up or login with your details

Forgot password? Click here to reset