Variational message passing for online polynomial NARMAX identification

We propose a variational Bayesian inference procedure for online nonlinear system identification. For each output observation, a set of parameter posterior distributions is updated, which is then used to form a posterior predictive distribution for future outputs. We focus on the class of polynomial NARMAX models, which we cast into probabilistic form and represent in terms of a Forney-style factor graph. Inference in this graph is efficiently performed by a variational message passing algorithm. We show empirically that our variational Bayesian estimator outperforms an online recursive least-squares estimator, most notably in small sample size settings and low noise regimes, and performs on par with an iterative least-squares estimator trained offline.Comment: 6 pages, 4 figures. Accepted to the American Control Conference 202

AIDA: An Active Inference-based Design Agent for Audio Processing Algorithms

In this paper we present AIDA, which is an active inference-based agent that iteratively designs a personalized audio processing algorithm through situated interactions with a human client. The target application of AIDA is to propose on-the-spot the most interesting alternative values for the tuning parameters of a hearing aid (HA) algorithm, whenever a HA client is not satisfied with their HA performance. AIDA interprets searching for the "most interesting alternative" as an issue of optimal (acoustic) context-aware Bayesian trial design. In computational terms, AIDA is realized as an active inference-based agent with an Expected Free Energy criterion for trial design. This type of architecture is inspired by neuro-economic models on efficient (Bayesian) trial design in brains and implies that AIDA comprises generative probabilistic models for acoustic signals and user responses. We propose a novel generative model for acoustic signals as a sum of time-varying auto-regressive filters and a user response model based on a Gaussian Process Classifier. The full AIDA agent has been implemented in a factor graph for the generative model and all tasks (parameter learning, acoustic context classification, trial design, etc.) are realized by variational message passing on the factor graph. All verification and validation experiments and demonstrations are freely accessible at our GitHub repository

RxInfer: A Julia package for reactive real-time Bayesian inference

<h2>RxInfer v2.14.2</h2> <p><a href="https://github.com/biaslab/RxInfer.jl/compare/v2.14.1...v2.14.2">Diff since v2.14.1</a></p> <p><strong>Merged pull requests:</strong></p> <ul> <li>Add prediction example (#184) (@albertpod)</li> <li>fix Aqua (#186) (@bvdmitri)</li> <li>Hierarchical Bayesian Linear Regression example (#187) (@bvdmitri)</li> <li>Fix import warnings (#189) (@bvdmitri)</li> </ul> <p><strong>Closed issues:</strong></p> <ul> <li>A forecasting/predicting example would be useful (#15)</li> <li>Document the ability to change the message passing rules and the <code>@meta</code> macro (#151)</li> <li>Tests are failing (due to Aqua) (#185)</li> <li>julia show warnings when using RxInfer for the first time (#188)</li> </ul>If you use this software, please cite our article in the Journal of Open Source Software

RxInfer: A Julia package for reactive real-time Bayesian inference

RxInfer v2.12.0 Diff since v2.11.3 Merged pull requests: Modify inference for predictions functionality (#51) (@albertpod) Move pkgeval badge to the developers documentation (#146) (@bvdmitri) Update README.md (#148) (@bvdmitri) Closed issues: Predictive posterior distributions (#58)If you use this software, please cite our article in the Journal of Open Source Software

RxInfer: A Julia package for reactive real-time Bayesian inference

<h2>RxInfer v2.14.0</h2> <p><a href="https://github.com/biaslab/RxInfer.jl/compare/v2.13.2...v2.14.0">Diff since v2.13.2</a></p> <p><strong>Merged pull requests:</strong></p> <ul> <li>Fixes warnings for unused datavars and addons (#124) (@bartvanerp)</li> <li>Add comparison to other packages (#162) (@albertpod)</li> <li>Fix weird font in the examples section (#170) (@bvdmitri)</li> <li>Introduce Bethe Free Energy (#173) (@ThijsvdLaar)</li> <li>Update CI.yml (#174) (@bvdmitri)</li> <li>Integration ExponentialFamily.jl (#176) (@bvdmitri)</li> </ul> <p><strong>Closed issues:</strong></p> <ul> <li>Add a section in the documentation that compares RxInfer to other well-known Bayesian inference packages (#71)</li> <li><code>initmarginals</code> broadcasting not working for <code>PointMass</code> (#101)</li> <li><code>warn</code> argument in <code>inference</code> function not affecting logs for unused vars (#104)</li> <li>@constraints simply ignore incomplete factorization constraints without any warning (#118)</li> <li>Section links in some examples are broken (#144)</li> <li>Introduce Bethe Free Energy and variational inference concept in the documentation (#152)</li> <li>Integration of ExponentialFamily.jl (#153)</li> <li>Kalman filtering example with system inputs (#166)</li> </ul>If you use this software, please cite our article in the Journal of Open Source Software