Yuling Yao’s Blog

In causal inference, we usually think of inverse-probability weighting (IPW) as using weights that are functions of treatment assignment $z$ and pretreatment variables $x$.

This week I received a shocking email from Jinlin, that my personal website was hacked—It was redirected to some scam webpage and all my blogs were missing.

Bertrand and I have recently finished our review paper on Bayesian prediction.

Last month I was at WashU campus for the Bayesian-for-nuclear-physics workshop.

The myth A few years ago I saw a StackOverflow question (but I cannot find it now): allegedly Andrew Gelman blogged that Bayesian models would not overfit and would only underfit (I also cannot find Andrew’s blog post now but I think what he meant by “Bay…

I came across this book called “Die with zero”.

In Bayesian computation, we use control variate to reduces Monte Carlo (MC) variance.

Consider a normal-normal model with vector data $y$ and scalar parameter $\mu$ and $\sigma$ written in the following stan code1: data { int<lower=0> N; vector[N] y; real<lower=0> tau; } parameters { real mu; real<lower=0> sigma; } model { mu ~…

Sometimes a model can be decomposed into modules and we may run inference separately.

Score matching Suppose that we observe a sequence of data $y={y_i \in R_m \mid 1\leq i \leq n}$ coming independently from an unknown distribution $p_{true}$; we would like to evaluate a forecast given by a probabilistic density function $p(y)$.