Simulation Based Inference

This week Shulin gave a tutorial on the use of Simulation Based Inferencing (SBI). SBI is used in many areas of science and engineering. The issue is that in general, in most of the simulations with Bayesian Inferencing there are intractable parameters that cannot be calculated which renders conventional statistical approaches inapplicable. SBI instead uses machine learning instead. Using sampling of data, a ML model can be trained to simulate the data distributions. And this model is tuned using the data distribution from actual cases to reduce the error in estimation.

In this talk Shulin discusses how these models are tuned and the distribution estimated. She goes over the various approaches to SBI including Monte Carlo simulation, Bayesian Approximation, and others. She also presents a lecture by Kyle Cramer on SBI.

*Links*

*Content*
00:00 Introduction
04:00 Topics
01:42 ML in simulation
06:55 SBI algorithm
12:30 SBI Demo
26:19 SMI Lecture

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#simulationbasedinference #sbi #bayesian #machinelearning