Monte Carlo integration

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• In Monte Carlo integration the integral to be calculated is estimated by a random value.
• The hit-or-miss Monte Carlo integration directly uses the interpretation of the integral as area.
• Monte Carlo integration is a method for using random sampling to estimate the values of integrals.
• Beating Monte Carlo Integration: a Nonasymptotic Study of Kernel Smoothing Methods.
• In this tutorial we will look at using Monte Carlo integration to draw from a bivariate normal distribution.
• Monte Carlo integration is a simple but rarely feasible method for estimating parameters using an assumed posterior distribution.
• The difficulty of Monte Carlo integration is that it requires that the posterior distribution can be directly drawn from.
• To better understand the behavior of our Monte Carlo integration, we can plot the posterior distribution of our parameters.
• Keep in mind that Monte Carlo integration is particularly useful for higher-dimensional integrals.
• However, we can extend Monte Carlo integration to random variables with arbitry PDFs.
• In mathematics, Monte Carlo integration is a technique for numerical integration using random numbers.
• Monte Carlo integration, on the other hand, employs a non-deterministic approach: each realization provides a different outcome.
• This chapter describes routines for multidimensional Monte Carlo integration.
• This data type defines a general function with parameters for Monte Carlo integration.
• This function allocates and initializes a workspace for Monte Carlo integration in dim dimensions.