Sep 18, 2019
1 min
Tag:
__casual__
This is trivial math question but it once bothers me in the construction of Dirichlet process using stick breaking process. The context is that if $V \sim beta(1, M)$ and $\theta \sim Dirichlet (\bar \alpha)$...

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Aug 31, 2019
4 mins
Tag:
__causal__
reserve-mode causal is hard.

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Aug 18, 2019
4 mins
Tag:
__zombie__
That said, xenophobism is xenophobism, no matter coated with a caucasian supremacism, or a seemingly-progressive popularism, or even when it is companied with a chauvinistic salute towards another group of straw men.

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Aug 05, 2019
2 mins
Tag:
__zombie__
No you cannot

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Aug 01, 2019
1 mins
Tag:
__zombie__
This is a jitt question in Andrew’s class: to (by hand) approximate the standard deviation of X/Y if X is N(5,1) and Y is N(1,3).

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Jul 30, 2019
18 mins
Tag:
__computation__
What if I run 4 chains and find $\hat R >>1$

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Jun 18, 2019
5 mins
Tag:
__computation__
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.

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Jun 17, 2019
1 mins
Tag:
__zombies__
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...

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Jun 14, 2019
9 mins
Tag:
__modeling__
__decision__
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.

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Jun 01, 2019
14 mins
Tag:
__modeling__
__causal__
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?

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May 30, 2019
4 mins
Tag:
__causal__
We can evaluate causal inference methods through simulations and based on predictive performance of ATE, but is it enough?

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May 22, 2019
3 mins
Tag:
__ad-hoc__
__computation__
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

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May 20, 2019
10 mins
Tag:
__computation__
We wrote a new paper on approximate Bayesian inference in deep network, with a similiar computation cost as point estimation.

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May 01, 2019
4 mins
Tag:
__politics__
A new book by the interesting candidate, Mayor Pete Buttigieg.

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Dec 01, 2018
11 mins
Tag:
__modeling__
__computation__
tl;nr.
No.

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