Today’s clip is from episode 146 of the podcast, with Ethan Smith.
Alex and Ethan discuss the application of Bayesian inference in high energy density physics, particularly in analyzing complex data sets. They highlight the advantages of Bayesian techniques, such as incorporating prior knowledge and managing uncertainties.
They also shares insights from an ongoing experimental project focused on measuring the equation of state of plasma at extreme pressures. Finally, Alex and Ethan advocate for best practices in managing large codebases and ensuring model reliability.
Get the full discussion here.
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Transcript
This is an automatic transcript and may therefore contain errors. Please get in touch if you're willing to correct them.
