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 glowingpython.blogspot.com
The Glowing Python: The Central Limit Theorem, a hands-on introduction
| | rasbt.github.io
mlxtend
3.9 parsecs away

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| | A library consisting of useful tools and extensions for the day-to-day data science tasks.
| | matbesancon.xyz
A Pythonic data science project: Part I |
3.6 parsecs away

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| | Learning by doing: detecting fraud on bank notes using Python in 3 steps.
| | nelari.us
Using inverse transform sampling to generate random numbers in a given distribution // My thought repository
2.8 parsecs away

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| | In inverse transform sampling, the inverse cumulative distribution function is used to generate random numbers in a given distribution. But why does this work? And how can you use it to generate random numbers in a given distribution by drawing random numbers from any arbitrary distribution?
| | gregorygundersen.com
Hamiltonian Monte Carlo
21.1 parsecs away

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| [AI summary] Hamiltonian Monte Carlo (HMC) is a Markov Chain Monte Carlo (MCMC) method that leverages Hamiltonian dynamics to generate samples from a probability distribution. Unlike traditional MCMC methods that rely on random walks, HMC introduces auxiliary momenta variables and simulates a physical system to produce correlated samples with higher efficiency. The method uses gradient information of the log density to guide the sampling process, enabling faster exploration of the target distribution and higher acceptance rates. The implementation of HMC involves defining the potential and kinetic energy functions, performing leapfrog integration to approximate the Hamiltonian dynamics, and using the Metropolis-Hastings acceptance criterion. An example using...