Online Probabilistic Model Identification using Adaptive Recursive MCMC

• URL: http://arxiv.org/abs/2210.12595v2
• Date: Thu, 19 Oct 2023 19:15:02 GMT
• Title: Online Probabilistic Model Identification using Adaptive Recursive MCMC
• Authors: Pedram Agand, Mo Chen, and Hamid D. Taghirad
• Abstract summary: We suggest the Adaptive Recursive Markov Chain Monte Carlo (ARMCMC) method. It eliminates the shortcomings of conventional online techniques while computing the entire probability density function of model parameters. We demonstrate our approach using parameter estimation in a soft bending actuator and the Hunt-Crossley dynamic model.
• Score: 8.465242072268019
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Although the Bayesian paradigm offers a formal framework for estimating the entire probability distribution over uncertain parameters, its online implementation can be challenging due to high computational costs. We suggest the Adaptive Recursive Markov Chain Monte Carlo (ARMCMC) method, which eliminates the shortcomings of conventional online techniques while computing the entire probability density function of model parameters. The limitations to Gaussian noise, the application to only linear in the parameters (LIP) systems, and the persistent excitation (PE) needs are some of these drawbacks. In ARMCMC, a temporal forgetting factor (TFF)-based variable jump distribution is proposed. The forgetting factor can be presented adaptively using the TFF in many dynamical systems as an alternative to a constant hyperparameter. By offering a trade-off between exploitation and exploration, the specific jump distribution has been optimised towards hybrid/multi-modal systems that permit inferences among modes. These trade-off are adjusted based on parameter evolution rate. We demonstrate that ARMCMC requires fewer samples than conventional MCMC methods to achieve the same precision and reliability. We demonstrate our approach using parameter estimation in a soft bending actuator and the Hunt-Crossley dynamic model, two challenging hybrid/multi-modal benchmarks. Additionally, we compare our method with recursive least squares and the particle filter, and show that our technique has significantly more accurate point estimates as well as a decrease in tracking error of the value of interest.

Related papers

• Variational Inference of Parameters in Opinion Dynamics Models [9.51311391391997]
  This work uses variational inference to estimate the parameters of an opinion dynamics ABM. We transform the inference process into an optimization problem suitable for automatic differentiation. Our approach estimates both macroscopic (bounded confidence intervals and backfire thresholds) and microscopic ($200$ categorical, agent-level roles) more accurately than simulation-based and MCMC methods.
  arXiv  Detail & Related papers  (2024-03-08T14:45:18Z)
• Online Variational Sequential Monte Carlo [49.97673761305336]
  We build upon the variational sequential Monte Carlo (VSMC) method, which provides computationally efficient and accurate model parameter estimation and Bayesian latent-state inference. Online VSMC is capable of performing efficiently, entirely on-the-fly, both parameter estimation and particle proposal adaptation.
  arXiv  Detail & Related papers  (2023-12-19T21:45:38Z)
• Formal Controller Synthesis for Markov Jump Linear Systems with Uncertain Dynamics [64.72260320446158]
  We propose a method for synthesising controllers for Markov jump linear systems. Our method is based on a finite-state abstraction that captures both the discrete (mode-jumping) and continuous (stochastic linear) behaviour of the MJLS. We apply our method to multiple realistic benchmark problems, in particular, a temperature control and an aerial vehicle delivery problem.
  arXiv  Detail & Related papers  (2022-12-01T17:36:30Z)
• Scalable Stochastic Parametric Verification with Stochastic Variational Smoothed Model Checking [1.5293427903448025]
  Smoothed model checking (smMC) aims at inferring the satisfaction function over the entire parameter space from a limited set of observations. In this paper, we exploit recent advances in probabilistic machine learning to push this limitation forward. We compare the performances of smMC against those of SV-smMC by looking at the scalability, the computational efficiency and the accuracy of the reconstructed satisfaction function.
  arXiv  Detail & Related papers  (2022-05-11T10:43:23Z)
• Distributionally Robust Models with Parametric Likelihood Ratios [123.05074253513935]
  Three simple ideas allow us to train models with DRO using a broader class of parametric likelihood ratios. We find that models trained with the resulting parametric adversaries are consistently more robust to subpopulation shifts when compared to other DRO approaches.
  arXiv  Detail & Related papers  (2022-04-13T12:43:12Z)
• Adversarial Robustness Verification and Attack Synthesis in Stochastic Systems [8.833548357664606]
  We develop a formal framework for adversarial robustness in systems defined as discrete time Markov chains (DTMCs) We outline a class of threat models under which adversaries can perturb system transitions, constrained by an $varepsilon$ ball around the original transition probabilities.
  arXiv  Detail & Related papers  (2021-10-05T15:52:47Z)
• Gaussian Process Latent Class Choice Models [7.992550355579791]
  We present a non-parametric class of probabilistic machine learning within discrete choice models (DCMs) The proposed model would assign individuals probabilistically to behaviorally homogeneous clusters (latent classes) using GPs. The model is tested on two different mode choice applications and compared against different LCCM benchmarks.
  arXiv  Detail & Related papers  (2021-01-28T19:56:42Z)
• Autoregressive Score Matching [113.4502004812927]
  We propose autoregressive conditional score models (AR-CSM) where we parameterize the joint distribution in terms of the derivatives of univariable log-conditionals (scores) For AR-CSM models, this divergence between data and model distributions can be computed and optimized efficiently, requiring no expensive sampling or adversarial training. We show with extensive experimental results that it can be applied to density estimation on synthetic data, image generation, image denoising, and training latent variable models with implicit encoders.
  arXiv  Detail & Related papers  (2020-10-24T07:01:24Z)
• Adaptive Subcarrier, Parameter, and Power Allocation for Partitioned Edge Learning Over Broadband Channels [69.18343801164741]
  partitioned edge learning (PARTEL) implements parameter-server training, a well known distributed learning method, in wireless network. We consider the case of deep neural network (DNN) models which can be trained using PARTEL by introducing some auxiliary variables.
  arXiv  Detail & Related papers  (2020-10-08T15:27:50Z)
• Identification of Probability weighted ARX models with arbitrary domains [75.91002178647165]
  PieceWise Affine models guarantees universal approximation, local linearity and equivalence to other classes of hybrid system. In this work, we focus on the identification of PieceWise Auto Regressive with eXogenous input models with arbitrary regions (NPWARX) The architecture is conceived following the Mixture of Expert concept, developed within the machine learning field.
  arXiv  Detail & Related papers  (2020-09-29T12:50:33Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.