rhymer

6 ms·

1.
by rhymer 10mo ago
Right, this is known as the inverse variance weighting https://en.wikipedia.org/wiki/Inverse-variance_weighting .
2.
by rhymer 2y ago
You're right, thanks for pointing that out. I missed adding the reference: Kammler DW. A First Course in Fourier Analysis. 2nd ed. Cambridge University Press; 2008. Figure 1.19. This visualization of the relationships between time and
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by rhymer 2y ago
This tool is fantastic! I was able to generate a Fourier-Poisson cube [0] in about 10 minutes, and the UI is incredibly intuitive. The focus on commutative diagrams, rather than a free-form canvas, is a brilliant design choice that keeps it
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by rhymer 2y ago
Be careful, the weight of Algorithm A by Efraimidis and Spirakis cannot be interpreted as the inclusion probability, and thus cannot be used in survey sampling to construct the Horvitz–Thompson estimator. See "Remarks on some misconcep
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by rhymer 2y ago
If the bus arrives on time, the arrival time would be [tau, 2 * tau, ..., N * tau]. One way to simulate "random" arrival time is to draw uniform points in the interval [0, N * tau]. It turns out the inter-arrival time generated th
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by rhymer 2y ago
Relevance: from the cited paper, the variance of the median estimator is proportional to 1/(n * f^2), where n is the sample size and f is the density at median. Two observations: 1. With sufficiently large n, you can control your varia
7.
by rhymer 2y ago
Asymptotic properties of quantile estimators are widely studied [1]. The key is to have a sufficiently large sample size. [1] Bahadur, R. R. (1966). A note on quantiles in large samples. Annals of Mathematical Statistics, 37, 577–580.
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by rhymer 2y ago
My takeaway is to avoid mixing the frequentist and Bayesian approaches. Choose one method: either follow the frequentist approach and avoid early data analysis, or use the Bayesian approach to compute posterior probabilities once data are a
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by rhymer 3y ago
I'm grateful for leetcode. Despite my background in electrical engineering, where I specialize in statistical signal processing, I never had the opportunity to delve into algorithm or data structure courses during my college years. In
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by rhymer 3y ago
1/ I think you are referring to pushforward measure ( https://en.wikipedia.org/wiki/Pushforward_measure ): the random variable "pushes" the probability measure to its codomain. 2/ pdf requires a stro
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by rhymer 3y ago
Agree, this is great! I wonder if there're some ML equivalent sites that present topics in a modular way?
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by rhymer 3y ago
Maybe "Probability via Expectation" by Peter Whittle https://link.springer.com/book/10.1007/978-1-4612-0509-8 or "Infinite Dimensional Analysis" by Charalambos Aliprantis and Kim Border https:
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by rhymer 4y ago
Just curious what are the deeper optimization topics you were hoping to see in the monograph?
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by rhymer 6y ago
Second this. Richard is a great lecturer. Highly recommend his lecture recordings. His winter 2019 lecture videos and materials can be found here: https://github.com/rmcelreath/statrethinking_winter2019
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by rhymer 7y ago
Unless you water them down like some populate science books, I don't understand how you can teach probability and statistics without calculus?
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by rhymer 7y ago
If I remember correctly the royalty was $7.5 per iPhone. Not sure how that alone contributes to the $150 price hike.
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by rhymer 7y ago
Or C-x * for calc-dispatch? I guess the point of this app is the input doesn't have to be precise. Not sure emacs can do that.
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by rhymer 7y ago
Yes but not directly. It is used in the Monte Carlo simulation part. In communication systems everything is complex :D
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by rhymer 7y ago
Last week I converted a simple side project in Python to Julia. It's a sequential Bayeisan estimation problem. A pleasant surprise that the Julia version runs 30x faster than the Python counter part. I am sure the Python code can be im
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by rhymer 8y ago
Fascinating! Such a great demonstration of Julia's strength. I wish more academic papers are written in this fashion or include a tutorial-like/reproducible post like this.