Lecture

Monte-Carlo Integration: Approximation and Variance

Description

This lecture covers Monte-Carlo integration, focusing on the approximation of expectations and variances using random sampling. It explains how to estimate the variance of a function and the desired precision through the number of draws. The instructor also discusses the error components in conditional choice models and the parameters estimation using maximum simulated likelihood.

In MOOCs (2)

Instructor

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Official source

https://mediaspace.epfl.ch/media/0_brq5lrnq

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Ontological neighbourhood

Statistics

Data collection: Statistical sampling

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