From 2277088223f2a7db1690def9f43d02203a795812 Mon Sep 17 00:00:00 2001 From: bartvanerp Date: Mon, 15 Jul 2024 08:48:11 +0000 Subject: [PATCH] deploy: f4040e4b53f872fafccd5bd2d6697fc757035786 --- index.html | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/index.html b/index.html index 052f27f..315c8db 100644 --- a/index.html +++ b/index.html @@ -29,7 +29,7 @@ data = (y = observations,) ) -

RxInfer is fast

RxInfer exploits the modularity of factor graphs to perform fast message passing-based probabilistic inference that scales linearly with the size of your model. We generally outperform state-of-the-art sampling-based packages by several orders of magnitude. The efficiency of RxInfer makes it easier to process streaming data sources in real time.

Check out some examples

Solve complex problems

Track hidden states of a dynamic system in real-time

Unveil real-time insights into dynamic systems with our software's prowess in tracking hidden states. By providing continuous monitoring and analysis, our tool empowers you to gain a deeper understanding of complex processes, enabling informed decision-making and proactive responses.

Smart navigation and collision avoidance

Stay in control, prevent collisions, and streamline routes effortlessly with RxInfer. Streamline your navigation experience and enhance safety.

Reactive reasoning and decision making with Active Inference framework

Enhance your decision-making process with the Active Inference framework. Designed to help you analyze incoming information in real-time, this tool enables you to make well-informed choices and adapt to changing situations effectively.

Watch our talk!

RxInfer has been presented on the JuliaCon 2023, the biggest conference of Julia developers. Check out our video!

Watch our presentation on JuliaCon

Community videos

Intro to RxInfer by Doggo.jl

Variational inference with RxInfer by Doggo.jl

Active Inference Symposium

Ecosystem

The RxInfer ecosystem stacks several Julia packages for running efficient Bayesian inference.

   Rocket.jl

Enables reactive programming in Julia for processing of asynchronous data streams.

RxInfer is fast

RxInfer exploits the modularity of factor graphs to perform fast message passing-based probabilistic inference that scales linearly with the size of your model. We generally outperform state-of-the-art sampling-based packages by several orders of magnitude. The efficiency of RxInfer makes it easier to process streaming data sources in real time.

Check out some examples

Solve complex problems

Track hidden states of a dynamic system in real-time

Unveil real-time insights into dynamic systems with our software's prowess in tracking hidden states. By providing continuous monitoring and analysis, our tool empowers you to gain a deeper understanding of complex processes, enabling informed decision-making and proactive responses.

Smart navigation and collision avoidance

Stay in control, prevent collisions, and streamline routes effortlessly with RxInfer. Streamline your navigation experience and enhance safety.

Reactive reasoning and decision making with Active Inference framework

Enhance your decision-making process with the Active Inference framework. Designed to help you analyze incoming information in real-time, this tool enables you to make well-informed choices and adapt to changing situations effectively.

Watch our talk!

RxInfer has been presented on the JuliaCon 2023, the biggest conference of Julia developers. Check out our video!

Watch our presentation on JuliaCon

Community videos

Fast Bayesian Inference with RxInfer.jl

Intro to RxInfer by Doggo.jl

Variational inference with RxInfer by Doggo.jl

Active Inference Symposium

Ecosystem

The RxInfer ecosystem stacks several Julia packages for running efficient Bayesian inference.

   Rocket.jl

Enables reactive programming in Julia for processing of asynchronous data streams.

   ReactiveMP.jl

Efficient, easily extensible and schedule-free reactive message passing-based inference engine.

   GraphPPL.jl

Powerful, user-friendly, graph-based specification of both model and inference constraints.