Robust estimation of high-order phase dynamics using Variational Bayes inference

2021 ◽  
Author(s):  
Fabio Fabozzi ◽  
Stephanie Bidon ◽  
Sebastien Roche
Keyword(s):  
Robust Estimation ◽  
High Order ◽  
Variational Bayes ◽  
Phase Dynamics ◽  
Bayes Inference

Variational Bayes Inference for the DINA Model

2020 ◽  
Vol 45 (5) ◽  
pp. 569-597
Author(s):  
Kazuhiro Yamaguchi ◽  
Kensuke Okada
Keyword(s):  
Real Data ◽  
Variational Bayes ◽  
Diagnostic Assessment ◽  
Model Parameters ◽  
Mcmc Method ◽  
Inference Method ◽  
Bayes Inference ◽  
Dina Model ◽  
Mcmc Estimation ◽  
Parameter Values

In this article, we propose a variational Bayes (VB) inference method for the deterministic input noisy AND gate model of cognitive diagnostic assessment. The proposed method, which applies the iterative algorithm for optimization, is derived based on the optimal variational posteriors of the model parameters. The proposed VB inference enables much faster computation than the existing Markov chain Monte Carlo (MCMC) method, while still offering the benefits of a full Bayesian framework. A simulation study revealed that the proposed VB estimation adequately recovered the parameter values. Moreover, an example using real data revealed that the proposed VB inference method provided similar estimates to MCMC estimation with much faster computation.

scholarly journals Multi-Ellipsoidal Extended Target Tracking with Variational Bayes Inference

2021 ◽  
Author(s):  
BARKIN TUNCER ◽  
Emre Özkan ◽  
Umut Orguner
Keyword(s):  
Target Tracking ◽  
Real Data ◽  
Variational Bayes ◽  
Inference Problem ◽  
Recursive Filtering ◽  
Conjugate Priors ◽  
Bayes Inference ◽  
Symmetric Positive Definite ◽  
Extended Target ◽  
Proposed Model

<div>In this work, we propose a novel extended target tracking algorithm, which is capable of representing a target or a group of targets with multiple ellipses. Each ellipse is modeled by an unknown symmetric positive-definite random matrix. The proposed model requires solving two challenging problems. First, the data association problem between the measurements and the sub-objects. Second, the inference problem that involves non-conjugate priors and likelihoods which needs to be solved within the recursive filtering framework. We utilize the variational Bayes inference method to solve the association problem and to approximate the intractable true posterior. The performance of the proposed solution is demonstrated in simulations and real-data experiments. The results show that our method outperforms the state-of-the-art methods in accuracy with lower computational complexity.</div>

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