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 learning community. As a result, interest in probabilistic programming frameworks has much increased over the past few years. 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. To this end, we developed "ForneyLab" (https://github.com/biaslab/ForneyLab.jl) as a Julia toolbox for message passing-based inference in FFGs. We show by example how ForneyLab enables automatic derivation of Bayesian signal processing algorithms, including algorithms for parameter estimation and model comparison. Crucially, due to the modular makeup of the FFG framework, both the model specification and inference methods are readily extensible in ForneyLab. In order to test this framework, we compared variational message passing as implemented by ForneyLab with automatic differentiation variational inference (ADVI) and Monte Carlo methods as implemented by state-of-the-art tools "Edward" and "Stan". In terms of performance, extensibility and stability issues, ForneyLab appears to enjoy an edge relative to its competitors for automated inference in state-space models.Comment: Accepted for publication in the International Journal of Approximate Reasonin

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. Active Inference (AIF) is a corollary of the FEP that describes how agents explore and exploit their environment by minimising an expected FE objective. In two related papers, we describe a scalable, epistemic approach to synthetic AIF agents, by message passing on free-form Forney-style Factor Graphs (FFGs). A companion paper (part I) introduces a Constrained FFG (CFFG) notation that visually represents (generalised) FE objectives for AIF. The current paper (part II) derives message passing algorithms that minimise (generalised) FE objectives on a CFFG by variational calculus. A comparison between simulated Bethe and generalised FE agents illustrates how synthetic AIF induces epistemic behaviour on a T-maze navigation task. With a full message passing account of synthetic AIF agents, it becomes possible to derive and reuse message updates across models and move closer to industrial applications of synthetic AIF

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 minimizing a free energy bound on Bayesian surprise. Inclusion of prior beliefs in the GM about desired states leads to active inference (ActInf). In this work, we aim to reveal connections between ActInf and stochastic optimal control. We reveal that, in contrast to standard cost and constraint-based solutions, ActInf gives rise to a minimization problem that includes both an information-theoretic surprise term and a model-predictive control cost term. We further show under which conditions both methodologies yield the same solution for estimation and control. For a case with linear Gaussian dynamics and a quadratic cost, we illustrate the performance of ActInf under varying system parameters and compare to classical solutions for estimation and control

Automating Model Comparison in Factor Graphs

Bayesian state and parameter estimation have been automated effectively in the literature, however, this has not yet been the case for model comparison, which therefore still requires error-prone and time-consuming manual derivations. As a result, model comparison is often overlooked and ignored, despite its importance. This paper efficiently automates Bayesian model averaging, selection, and combination by message passing on a Forney-style factor graph with a custom mixture node. Parameter and state inference, and model comparison can then be executed simultaneously using message passing with scale factors. This approach shortens the model design cycle and allows for the straightforward extension to hierarchical and temporal model priors to accommodate for modeling complicated time-varying processes

Simulating Active Inference Processes by Message Passing

The free energy principle (FEP) offers a variational calculus-based description for how biological agents persevere through interactions with their environment. Active inference (AI) is a corollary of the FEP, which states that biological agents act to fulfill prior beliefs about preferred future observations (target priors). Purposeful behavior then results from variational free energy minimization with respect to a generative model of the environment with included target priors. However, manual derivations for free energy minimizing algorithms on custom dynamic models can become tedious and error-prone. While probabilistic programming (PP) techniques enable automatic derivation of inference algorithms on free-form models, full automation of AI requires specialized tools for inference on dynamic models, together with the description of an experimental protocol that governs the interaction between the agent and its simulated environment. The contributions of the present paper are two-fold. Firstly, we illustrate how AI can be automated with the use of ForneyLab, a recent PP toolbox that specializes in variational inference on flexibly definable dynamic models. More specifically, we describe AI agents in a dynamic environment as probabilistic state space models (SSM) and perform inference for perception and control in these agents by message passing on a factor graph representation of the SSM. Secondly, we propose a formal experimental protocol for simulated AI. We exemplify how this protocol leads to goal-directed behavior for flexibly definable AI agents in two classical RL examples, namely the Bayesian thermostat and the mountain car parking problems

Monitoring recently acquired HIV infections in Amsterdam, The Netherlands:The attribution of test locations

Background:  Surveillance of recent HIV infections (RHI) using an avidity assay has been implemented at Dutch sexual health centres (SHC) since 2014, but data on RHI diagnosed at other test locations is lacking. Setting:  Implementation of the avidity assay in HIV treatment clinics for the purpose of studying RHI among HIV patients tested at different test locations. Methods: We retrospectively tested leftover specimens from newly diagnosed HIV patients in care in 2013–2015 in Amsterdam. Avidity Index (AI) values ≤0.80 indicated recent infection (acquired ≤6 months prior to diagnosis), and AI > 0.80 indicated established infection (acquired >6 months prior to diagnosis). An algorithm for RHI was applied to correct for false recency. Recency based on this algorithm was compared with recency based on epidemiological data only. Multivariable logistic regression analysis was used to identify factors associated with RHI among men who have sex with men (MSM).Results: We tested 447 specimens with avidity; 72% from MSM. Proportions of RHI were 20% among MSM and 10% among heterosexuals. SHC showed highest proportions of RHI (27%), followed by GPs (15%), hospitals (5%), and other/unknown locations (11%) (p < 0.001). Test location was the only factor associated with RHI among MSM. A higher proportion of RHI was found based on epidemiological data compared to avidity testing combined with the RHI algorithm. Conclusion:  SHC identify more RHI infections compared to other test locations, as they serve high-risk populations and offer frequent HIV testing. Using avidity-testing for surveillance purposes may help targeting prevention programs, but the assay lacks robustness and its added value may decline with improved, repeat HIV testing and data collection

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. Most approaches for identifying transmission pairs do not account for the time that passed since divergence of pathogen variants in individuals, which is problematic in viruses with high within-host evolutionary rates. This is prompting us to consider possible transmission pairs in terms of phylogenetic data and additional estimates of time since infection derived from clinical biomarkers. We develop Bayesian mixture models with an evolutionary clock as signal component and additional mixed effects or covariate random functions describing the mixing weights to classify potential pairs into likely and unlikely transmission pairs. We demonstrate that although sources cannot be identified at the individual level with certainty, even with the additional data on time elapsed, inferences into the population-level sources of transmission are possible, and more accurate than using only phylogenetic data without time since infection estimates. We apply the approach to estimate age-specific sources of HIV infection in Amsterdam MSM transmission networks between 2010-2021. This study demonstrates that infection time estimates provide informative data to characterize transmission sources, and shows how phylogenetic source attribution can then be done with multi-dimensional mixture models

Female Sex and IL28B, a Synergism for Spontaneous Viral Clearance in Hepatitis C Virus (HCV) Seroconverters from a Community-Based Cohort

BACKGROUND & AIMS: Since acute hepatitis C virus (HCV) infection is often asymptomatic, it is difficult to examine the rate and determinants of spontaneous clearance. Consequently, these studies are subject to bias, which can potentially lead to biased rates of viral clearance and risk estimates. We evaluated determinants of spontaneous HCV clearance among HCV seroconverters identified in a unique community-based cohort. METHODS: Subjects were 106 drug users with documented dates of HCV seroconversion from the Amsterdam Cohort Study. Logistic regression was used to examine sociodemographic, behavioral, clinical, viral and host determinants, measured around acute infection, of HCV clearance. RESULTS: The spontaneous viral clearance rate was 33.0% (95% confidence interval (CI) 24.2-42.8). In univariate analyses female sex and fever were significantly associated with spontaneous clearance. The favorable genotypes for rs12979860 (CC) and rs8099917 (TT) were associated with spontaneous clearance, although borderline significant. In multivariate analysis, females with the favorable genotype for rs12979860 (CC) had an increased odds to spontaneously clear HCV infection (adjusted OR 6.62, 95% 2.69-26.13), whereas females with the unfavorable genotype were as likely as men with the favorable and unfavorable genotype to clear HCV. Chronic Hepatitis B infection and absence of HIV coinfection around HCV seroconversion also favor HCV clearance. CONCLUSIONS: This study shows that co-infection with HIV and HBV and genetic variation in the IL28B region play an important role in spontaneous clearance of HCV. Our findings suggest a possible synergistic interaction between female sex and IL28B in spontaneous clearance of HCV

Direct-Acting Antiviral Treatment for Hepatitis C Genotypes Uncommon in High-Income Countries:A Dutch Nationwide Cohort Study

Background. The majority of hepatitis C virus (HCV) infections are found in low- and middle-income countries, which harbor many region-specific HCV subtypes. Nevertheless, direct-acting antiviral (DAA) trials have almost exclusively been conducted in high-income countries, where mainly epidemically spread HCV subtypes are present. Recently, several studies have demonstrated suboptimal DAA efficacy for certain nonepidemic subtypes, which could hamper global HCV elimination. Therefore, we aimed to evaluate DAA efficacy in patients treated for a nonepidemic HCV genotype infection in the Netherlands. Methods. We performed a nationwide retrospective study including patients treated with interferon-free DAAs for an HCV genotype other than 1a/1b/2a/2b/3a/4a/4d. The genotype was determined by NS5B region phylogenetic analysis. The primary end point was SVR-12. If stored samples were available, NS5A and NS5B sequences were obtained for resistance-associated substitutions (RAS) evaluation. Results. We included 160 patients, mainly infected with nonepidemic genotype 2 (41%) and 4 (31%) subtypes. Most patients were from Africa (45%) or South America (24%); 51 (32%) were cirrhotic. SVR-12 was achieved in 92% (140/152) of patients with available SVR-12 data. Only 73% (8/11) genotype 3-infected patients achieved SVR-12, the majority being genotype 3b patients with 63% (5/8) SVR. Regardless of SVR, all genotype 3b patients had 30K and 31M RAS. Conclusions. (T)he DAA efficacy we observed in most nonepidemic genotypes in the Netherlands seems reassuring. However, the low SVR-12 rate in subtype 3b infections is alarming, especially as it is common in several HCV-endemic countries. Alongside earlier results, our results indicate that a remaining challenge for global HCV elimination is confirming and monitoring DAA efficacy in nonepidemic genotypes

Efficacy of serology driven “test and treat strategy” for eradication of H. pylori in patients with rheumatic disease in the Netherlands

The treatment of choice of H. pylori infections is a 7-day triple-therapy with a proton pump inhibitor (PPI) plus amoxicillin and either clarithromycin or metronidazole, depending on local antibiotic resistance rates. The data on efficacy of eradication therapy in a group of rheumatology patients on long-term NSAID therapy are reported here. This study was part of a nationwide, multicenter RCT that took place in 2000–2002 in the Netherlands. Patients who tested positive for H. pylori IgG antibodies were included and randomly assigned to either eradication PPI-triple therapy or placebo. After completion, follow-up at 3 months was done by endoscopy and biopsies were sent for culture and histology. In the eradication group 13% (20/152, 95% CI 9–20%) and in the placebo group 79% (123/155, 95% CI 72–85%) of the patients were H. pylori positive by histology or culture. H. pylori was successfully eradicated in 91% of the patients who were fully compliant to therapy, compared to 50% of those who were not (difference of 41%; 95% CI 18–63%). Resistance percentages found in isolates of the placebo group were: 4% to clarithromycin, 19% to metronidazole, 1% to amoxicillin and 2% to tetracycline