In my previous blog post on hierarchical stacking, reader “Chaos” pointed to me Gavin Brown’s Ph.D. thesis on Negative Correlation (NC) Learning which had a good characterization of the importance of diversity to stacking or...

I get an interview feedback from a company. I initially thought my interviews went well but it turned out that the company had a different opinion. Generally, it would be silly for me to post...

The term online update here is referred to updating a statistical model after certain modeled outcome is observed. A concrete example is in election forecast: the state election result comes in sequence, and that is...

In a recent paper I wrote, I discussed a few open questions on ensemble methods: Both BMA and stacking are restricted to a linear mixture form, would it be beneficial to consider other aggregation forms...

Recently I have gone through a few industry job applications. Here is a few examples that manifest how amusing this procedure can become

Depends who you ask.

I was reading Peter Bühlmann’s statistical science article “Invariance, Causality and Robustness”. To be fair, he gave a short course in 2020 here in Columbia, but after reading this paper I guess I did not...

It comes a lot that we often summarize a continuous distribution (often, posterior distribution of parameter estimation or of predictions) by a point mass (or a sharpe spike) for (1) computation or memory cost, (2)...

Cramér–Rao lower bound I will not call myself a theoretic statistician but sometimes I still find mathematical statistics amusing especially when they have practical implications. To start this blog post, I will go from Cramér–Rao...

The likelihood principle is often phrase as an axiom in Bayesian statistics. My interpretation of the likelihood principle reads:

This comes a lot in Monte Carlo computation: we are only given finite draws but we want to compute extreme quantiles.

Today I was reading the book “Discrete Distribution” by Johnson and Kotz. I did not realize it has a newer version until I started this blog post—-the edition I read was published in 1969 by...

This is wrong. Indeed it can be opposite.

In a predictive paradigm, no one really cares about how I obtain the estimation or the prediction. It can come from some MLE, MAP of risk minimization, or some Bayes procedure. Also, when we talk...

in general B-spline is sensitive to the boundary knots, while the unknown support of latent variable models amplifies such sensitivity.