Building probabilistic models coupled with simulation-based inference using Bayesian program synthesis method while bringing human knowledge into
the machine learning system.
BayesLearn Systems is making it possible for everyone to do rigorous statistical analysis, even when their data is expensive, sparse, or complex. With the active contribution of our mother company TeslaDuo Group's Innovation Lab (Probabilistic Programming Project), BayesLearn Systems within over 3-year time period has created one of the first AIs that creates probabilistic & generative models automatically, makes simulation-based inference to automate analysis of tabular data.
BayesLearn's mission is to accelerate the adoption of AI and make Probabilistic programming Bayesian inference broadly accessible.
BayesLearn Systems provides businesses with a platform for building and deploying data & machine learning models to helps companies solve challenges by finding the best predictive model for their data and offering probabilistic answers to ad hoc queries.
The idea is to build Bayesian probabilistic programs that model the data and play the role of simulators allowing randomness and simulating systems and the generative story of your data and how your data came to be via probabilistic programs & Bayesian synthesis.
Simulation is a key technology in the modern world to understand business problems of any kind.
Each program will try as many possibilities through random variable in the model as it can to try to match the data which gives the posterior distribution as output. Determinism is also part of our models especially when we have pretty good idea of the story that created the data.
The idea is that we can take knowledge, put it into a machine learning system and then start getting results in the most meaningful ways possible. It is more human-like to understand things that learn and adapt in ways that people do, and not in ways that require you to get all kind of data and put it into the system. Put all these things together with push for explainability and a push for de-biasing to get what we are doing. It takes minutes not once or days to install it and mostly, it doesn't take millions of data points.
Common-sense knowledge that offers breakthrough improvements in accuracy & efficiency and creates a system where human intelligence can easily guide the program.
Our machine learning, is a next-generation system that represents a fundamental improvement in how machines can understand the human language and the knowledge expressed through it. It gives you the benefits of unsupervised learning (limited to no training data, fast implementation) while solving some of the real world problems with current offerings.
Automatic data modeling using Bayesian synthesis of probabilistic programs.
Data is the basic building block of everything you do in modeling. We are specialized in building large scale Bayesian models and systems learning essentially the story about how the data came to be. In other words we do automated data modeling via Bayesian synthesis of probabilistic programs from the observed data using Python and/or a bunch of Domain-Specific Languages (DSLs).
Using Bayesian probability as a mathematical framework of choice to refine predictions about the world by means of acquired experience.
Bayesian methods remain the best way to express the probability theory & the theory of uncertainty. Probabilistic programming coupled with a Bayesian approach is the key to build solid data & machine learning models and make great approximations & accurate predictions by adjusting parameters, as far as real world data are concerned. Indeed, probabilistic programming is a powerful abstraction layer for Bayesian inference, separating the model learning, suitable for probabilistic programming & based on maximum marginal likelihood, and the inference part of the problem.
Place Champs de Mars, 5
Bastion Tower - Level 20
1050 Brussels, Belgium
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New York, NY 10065, USA
Starting with your URL.
Allowing randomness and simulating systems via probabilistic programs & Bayesian synthesis.
The idea is to build Bayesian probabilistic programs that model the data and that play the role of simulators able to simulate the generative story of your sparse data & how your data came to be. Each program will try as many possibilities through random variable in the model as it can to try to match the data which gives the posterior distribution as output. Determinism will also be part of the model when we have pretty good idea of the story that created the data.