Integration with an Adaptive Harmonic Mean Algorithm

Understand

Numerically estimating the integral of functions in high dimensional spaces is a non-trivial task.

• A oft-encountered example is the calculation of the marginal likelihood in Bayesian inference, in a context where a sampling algorithm such as a Markov Chain Monte Carlo provides samples of the function.
• We present an Adaptive Harmonic Mean Integration (AHMI) algorithm.
• Given samples drawn according to a probability distribution proportional to the function, the algorithm will estimate the integral of the function and the uncertainty of the estimate by applying a harmonic mean estimator to adaptively chosen regions of the parameter space.