MCMC using Hamiltonian dynamics · HackerPapers discussions

MCMC using Hamiltonian dynamics — mentioned by yt-sdb

Fri, 22 Oct 2021 22:11:40 GMT

Probabilistic programming uses computer science techniques to do automated statistical modeling. For example, imagine I have a coin, and I want to discover if it is biased, i.e. if it lands on heads more often than tails. In a probabilistic programming framework, I can express my model as a simple Bernoulli model, `x ~ Bernoulli(p)`, and then automatically estimate the bias parameter `p` given some data (do "inference"). You can easily do this calculation by hand or in Python, but this does not generalize to more complex real-world scenarios. For complex probabilistic models, we must rely on…

Paper and discussion history. A mention is not an endorsement.

MCMC using Hamiltonian dynamics — mentioned by activatedgeek

Thu, 11 Apr 2019 14:43:20 GMT

Markov chains in essence are simple. Instead of diverging and reading all the theory, I'd recommend do it on a need basis. Learn as you go. So pick up a problem and move ahead. I don't think it is fruitful to just learn everything about Markov Chains just for the sake of it. Markov Chain Monte Carlo to sample from probability distributions is a good start - https://arxiv.org/abs/1206.1901 if you are into sampling.

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MCMC using Hamiltonian dynamics — HN submission

Mon, 14 May 2018 12:35:28 GMT

MCMC using Hamiltonian dynamics (2012)

2 points; 0 discussion replies on HN. Paper and source history. A submission is not an endorsement.

MCMC using Hamiltonian dynamics — mentioned by soVeryTired

Tue, 28 Feb 2017 14:51:11 GMT

Radford Neal's expository paper [1] on HMC is a beautiful overview of the area, and honestly one of the most enlightening papers I've ever read. Conceptually, HMC really isn't that difficult. The broad idea is as follows: Borrowing from statistical physics, it's possible to link energy and probability as E(x) = exp(-P(x)). So you can interpret your MCMC target as an "energy landscape". Highpoints in the landscape correspond to low probabilities in the target distribution, and valleys to high-probability regions. Now add a source of kinetic energy (an auxiliary probability distribution), and…

Paper and discussion history. A mention is not an endorsement.