Search Results for author:
Found 10 papers, 3 papers with code
Sources of HIV infections among MSM with a migration background: a viral phylogenetic case study in Amsterdam, the Netherlands
Methods: We used partial HIV consensus sequences with linked demographic and clinical data from the opt-out ATHENA cohort of people with HIV in the Netherlands to quantify population-level sources of transmission to Dutch-born and foreign-born Amsterdam men who have sex with men (MSM) between 2010-2021.
Realising Synthetic Active Inference Agents, Part I: Epistemic Objectives and Graphical Specification Language
Active Inference (AIF) is a corollary of the FEP that specifically details how systems that are able to plan for the future (agents) function by minimising particular free energy functionals that incorporate information seeking components.
Automating Model Comparison in Factor Graphs
Bayesian state and parameter estimation have been automated effectively in a variety of probabilistic programming languages.
Realising Synthetic Active Inference Agents, Part II: Variational Message Updates
The Free Energy Principle (FEP) describes (biological) agents as minimising a variational Free Energy (FE) with respect to a generative model of their environment.
Bayesian mixture models for phylogenetic source attribution from consensus sequences and time since infection estimates
In stopping the spread of infectious diseases, pathogen genomic data can be used to reconstruct transmission events and characterize population-level sources of infection.
Estimating the potential to prevent locally acquired HIV infections in a UNAIDS Fast-Track City, Amsterdam
Utilising molecular and clinical data of the ATHENA observational HIV cohort, our primary aims are to estimate the proportion of undiagnosed HIV infections and the proportion of locally acquired infections in Amsterdam in 2014-2018, both in MSM and heterosexuals and Dutch-born and foreign-born individuals.
Active Inference and Epistemic Value in Graphical Models
The AIF literature describes multiple VFE objectives for policy planning that lead to epistemic (information-seeking) behavior.
Chance-Constrained Active Inference
We illustrate how chance-constrained ActInf weights all imposed (prior) constraints on the generative model, allowing e. g., for a trade-off between robust control and empirical chance constraint violation.
A Factor Graph Approach to Automated Design of Bayesian Signal Processing Algorithms
This paper explores a specific probabilistic programming paradigm, namely message passing in Forney-style factor graphs (FFGs), in the context of automated design of efficient Bayesian signal processing algorithms.
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.
