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Meta’s Bean Machine: The Hot Topic In Probabilistic Programming

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Probabilistic modelling

Four major steps are entailed in generating successful probabilistic modelling through the Bean Machine. The modelling is based on generative techniques, the data collected from Python dictionaries where it is associated with random variables. The learning step improves the model’s knowledge based on observations, and the results are stored for further analysis.

Through probabilistic modelling, engineers and data scientists can account for random events in future predictions while measuring different uncertainties. This method is preferred because it offers uncertainty estimation, expressivity, and interpretability facilities. Let’s discuss these.

Scientists accidentally discover a first-of-its-kind mineral from deep inside Earth

WHEN OLIVER TSCHAUNER AND COLLEAGUES dusted off a sample of volcano-ejected diamond found in a South African mine, they had no idea that they were holding the first-ever natural sample of a new high-pressure mineral from deep within Earth.

While minuscule, researchers predict that this mineral is responsible for the movement of crucial components like rare earth metals and radioactive isotopes through the Earth’s mantle. To figure this out, the team turned to X-ray beams and lasers.


Using x-rays and lasers, scientists have analyzed the second-ever high-pressure mineral extracted from the earth’s lower mantle.