Like importance sampling, ABC is in principle immune to metastability that MCMC has to suffer, but ABC is also problematic as we have no idea what it will converge to when the model is misspecified.

When I talked to someone about the old proof the invariance of odds ratio in a respropective sampling, I mentioned the estimation of $q(x)$ is achieved by its non-parametric MLE or its empirical distribution (see...

Some loss functions are invariant under domain adaption, which suggests we can indeed learn the optimal model of the population from a non-representative sample without sacrifice from the extrapolation.

The odds ratio from a case-control study is exactly the same as in a cohort study, therefore I could fit a retrospective logistic regression as if it is prospective and report its MLE or Bayesian posterior distribution. But considering the sampling distribution shift, should I reweight it regardless?

We can evaluate causal inference methods through simulations and based on predictive performance of ATE, but is it enough?

Most statisticians-orientated introduction to optimal transport theory starts with the motivation: we can always obtain a one-D distribution $f(\theta)$ through its inverse cdf transformation

We wrote a new paper on approximate Bayesian inference in deep network, with a similiar computation cost as point estimation.

tl;nr. No.

I have recenetly been reading a textbook on decoupling. Decoupling literally means “from dependence to independence”. I initially though the book can help me understand some properties of self-normalized importance sampling, but it turns out...

I am starting to write my blog. And it apparently took more time to become convinced than actually begining, just as many other things. As a technical issue, I am now using Github Page to...