## integral theorems for Monte Carlo

Posted in Books, pictures, Statistics with tags , , , , , , , on August 12, 2021 by xi'an

and Stephen G. Walker have just arXived a paper on the use of (Fourier) integral theorems for Monte Carlo estimators, following the earlier entry of Parzen: namely that for any integrable function, $m(y)=\frac{1}{(2\pi)^d}\int_{\mathbb R^d}\int_{\mathbb R^d}\cos(s^\text{T}(y-x))m(x)\text dx\text ds$

which can be turned into an estimator of a density m based on a sample from m. This identity can be rewritten as $m(y)=\lim_{R\to\infty}\frac{1}{\pi^d}\int_{\mathbb R^d}\prod_{i=1}^d\dfrac{\sin(R(y_i-x_i))}{y_i-x_i}\;m(x)\,\text dx$

and the paper generalises this identity to all cyclic functions. Even though it establishes that sin is the optimal choice. After reading this neat result, I however remain uncertain on how this could help with Monte Carlo integration.

## visualising bias and unbiasedness

Posted in Books, Kids, pictures, R, Statistics, University life with tags , , , , , , , , , on April 29, 2019 by xi'an A question on X validated led me to wonder at the point made by Christopher Bishop in his Pattern Recognition and Machine Learning book about the MLE of the Normal variance being biased. As it is illustrated by the above graph that opposes the true and green distribution of the data (made of two points) against the estimated and red distribution. While it is true that the MLE under-estimates the variance on average, the pictures are cartoonist caricatures in their deviance permanence across three replicas. When looking at 10⁵ replicas, rather than three, and at samples of size 10, rather than 2, the distinction between using the MLE (left) and the unbiased estimator of σ² (right). When looking more specifically at the case n=2, the humongous variability of the density estimate completely dwarfs the bias issue: Even when averaging over all 10⁵ replications, the difference is hard to spot (and both estimations are more dispersed than the truth!): 