Article-Journal

Variational Message Passing and Local Constraint Manipulation in Factor Graphs
Accurate evaluation of Bayesian model evidence for a given data set is a fundamental problem in model development. Since evidence …
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Message Passing-Based Inference for Time-Varying Autoregressive Models
Time-varying autoregressive (TVAR) models are widely used for modeling of non-stationary signals. Unfortunately, online joint …
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Chance-constrained active inference
Active inference (ActInf) is an emerging theory that explains perception and action in biological agents in terms of minimizing a free …
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Application of the free energy principle to estimation and control
Based on a generative model (GM) and beliefs over hidden states, the free energy principle (FEP) enables an agent to sense and act by …
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Simulating Active Inference Processes by Message Passing
The free energy principle (FEP) offers a variational calculus-based description for how biological agents persevere through …
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A Factor Graph Approach to Automated Design of Bayesian Signal Processing Algorithms
The benefits of automating design cycles for Bayesian inference-based algorithms are becoming increasingly recognized by the machine …
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A factor graph description of deep temporal active inference
Active inference is a corollary of the Free Energy Principle that prescribes how self-organizing biological agents interact with their …
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The graphical brain: Belief propagation and active inference
This paper considers functional integration in the brain from a computational perspective. We ask what sort of neuronal message passing …
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A Probabilistic Modeling Approach to Hearing Loss Compensation
Hearing Aid (HA) algorithms need to be tuned (“fitted”) to match the impairment of each specific patient. The lack of a fundamental HA …
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