ReactiveMP.jl: a Julia package for automatic Bayesian inference on a factor graph with reactive message passing.

ReactiveMP v3.7.1 Diff since v3.7.

ReactiveMP.jl: a Julia package for automatic Bayesian inference on a factor graph with reactive message passing

ReactiveMP v3.6.1 Diff since v3.6.

ReactiveMP.jl: a Julia package for automatic Bayesian inference on a factor graph with reactive message passing

ReactiveMP v3.9.0 Diff since v3.8.1 Closed issues: Add soft dot product (#231) Memory Addon can break displaying posteriors in REPL (#306) Gamma distribution pdf producing NaNs (#314) Merged pull requests: Add multiplication rules for two univariate random variables (#298) (@HoangMHNguyen) do not override Base.nameof (#300) (@bvdmitri) Add tests for AR node (#301) (@albertpod) Reimplement @test_rules and @test_marginalrules (#302) (@bvdmitri) Add SoftDot node (#305) (@abpolym) fix erroneous Base.show method for MemoryAddon (#307) (@bvdmitri) Update test_marginals.jl (#308) (@albertpod) Fix GammaShapeRate pdf (#315) (@Nimrais) generalize bernoulli update rule and improve robustness (#316) (@bartvanerp) use Julia 1.9 in tests (#319) (@bvdmitri) Add Extensions support from Julia 1.9 (#320) (@bvdmitri) add cholinv-family methods for UniformScaling (#322) (@bvdmitri

ReactiveMP.jl: a Julia package for automatic Bayesian inference on a factor graph with reactive message passing

ReactiveMP v3.7.3 Diff since v3.7.2 Closed issues: Some tests for CVI do not converge (#280) @average_energy should use meta::Nothing by default (#293) Merged pull requests: 283 the mean field rules for the transition node are not generic (#289) (@wouterwln) Make CVI tests less confusing (#291) (@Nimrais) fix: change default meta type in average energy from Any to Nothing (#295) (@bvdmitri) Add rules for univariate normal (#297) (@albertpod

antigenomics/vdjdb-db: [CONTENT, INFRASTRUCTURE] New studies & docker

Added records: PMID:34811538 - TAAs PMID:33945786 - SARS-CoV-2/HCoV-HKU1 cross-reactive PMID:34880106 - many HIV sequences PMID:34793243 - lots of SARS-CoV-2 epitopes/TCRs, similar to 10X compendium Updated PDB list Infrastructure: On the way to Docker for database build/releas

ReactiveMP.jl: a Julia package for automatic Bayesian inference on a factor graph with reactive message passing

ReactiveMP v3.9.2 Diff since v3.9.1 Closed issues: Update README (#101) mean(f, samplelist) is not defined (#325) Merged pull requests: Temporary fix for incompatibility @call_rule with addons (#331) (@bartvanerp) Define the mean function for a sample list with respect to an arbitrary function (#333) (@bvdmitri

ReactiveMP.jl: a Julia package for automatic Bayesian inference on a factor graph with reactive message passing

ReactiveMP v3.8.1 Diff since v3.8.

ReactiveMP.jl: a Julia package for automatic Bayesian inference on a factor graph with reactive message passing.

ReactiveMP v3.7.2 Diff since v3.7.1 Closed issues: Excessive/unnecessary allocations in the call_rule_make_node and MessageMapping (#284) Merged pull requests: Remove Kernel GCV node (#285) (@albertpod) chore(): improve allocation profile for rule dispatching (#290) (@bvdmitri

ReactiveMP.jl: a Julia package for automatic Bayesian inference on a factor graph with reactive message passing

ReactiveMP v3.9.1 Diff since v3.9.0 Closed issues: Add multiplication of two Gaussian messages (#265) average energy does not check arguments and does not provide a useful error (#318) approximate_as_samplelist procedure uses eltype instead of paramfloattype (#323) Non-linear node fails for single multivariate input (#328) Merged pull requests: add check for input arguments in the rules (#321) (@bvdmitri) Add debug addons (#326) (@bartvanerp) Make the sample list approximation more generic (#327) (@bvdmitri) Fix Linearization method for multivariate input univariate output (#330) (@albertpod

ReactiveMP.jl demo v1.3.2

We present our toolbox that is aimed for automatic reactive variational Bayesian inference in state-space models. For a particular set of probabilistic models our implementation executes faster, scales better, and opens new possibilities for further research in this area. We believe that our work will drive more research in the Bayesian inference area and will allow for more sophisticated probabilistic models to be applied in practice