A Review of Importance Sampling in Simulation

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This paper presents an examination of importance sampling as a variance reduction technique.
Importance sampling is a powerful and widely used technique in stochastic simulation, particularly valued for its potential to dramatically reduce variance when estimating expectations or probabilities, especially in the context of rare events. This paper presents an examination of importance sampling as a variance reduction technique and provides an account of its theoretical foundations, practical implementation strategies, assumptions for validity, advantages, disadvantages, and mathematical derivation. Additionally, an R-based example is provided, demonstrating significant variance reduction.

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Johann Markus Schauerhuber
During his studies and academic resp. professional activities Prof. Dr. Dr. Johann Markus Schauerhuber has been intensively involved in statistical programming, stochastic, risk theory, simulation and mathematical modeling. Most of his professional experience has been gained in the university domain as an academic director, postdoc lecturer / researcher and in government authorities. Email: jm_schauerhuber@gmx.at

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Importance Sampling R Variance Reduction

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