เปรียบเทียบวิธี

	Dynamic Hamiltonian Monte Carlo	การอนุมานแบบแปรผัน
สาขาวิชา	เบย์	เบย์
ตระกูล	Bayesian methods	Bayesian methods
ปีกำเนิด	2014	1999
ผู้ริเริ่ม	Matthew D. Hoffman and Andrew Gelman	Jordan, Ghahramani, Jaakkola & Saul
ประเภท	adaptive MCMC sampler	Approximate Bayesian inference
แหล่งต้นตำรับ	Hoffman, M. D. & Gelman, A. (2014). The No-U-Turn Sampler: Adaptively setting path lengths in Hamiltonian Monte Carlo. Journal of Machine Learning Research, 15(1), 1593–1623.	Jordan, M. I., Ghahramani, Z., Jaakkola, T. S., & Saul, L. K. (1999). An introduction to variational methods for graphical models. Machine Learning, 37(2), 183–233.
ชื่อเรียกอื่น	Dynamic HMC, NUTS, No-U-Turn Sampler, adaptive HMC	VI, variational Bayes, VB, mean-field variational inference
ที่เกี่ยวข้อง	5	4
สรุป	Dynamic Hamiltonian Monte Carlo — widely known as the No-U-Turn Sampler (NUTS) — is an adaptive extension of Hamiltonian Monte Carlo that automatically selects the number of leapfrog integration steps during each MCMC transition, removing the need to hand-tune the most sensitive tuning parameter of standard HMC. It is the default sampler in Stan and PyMC and is suitable for continuous, differentiable posterior distributions of moderate to high dimension.	Variational inference (VI) is a family of techniques that turn Bayesian posterior computation into an optimisation problem. Instead of drawing samples from the exact posterior — as Markov chain Monte Carlo does — VI posits a simpler, tractable family of distributions and finds the member of that family closest to the true posterior by maximising the evidence lower bound (ELBO). Introduced in its modern graphical-model form by Jordan, Ghahramani, Jaakkola and Saul (1999) and given a comprehensive statistical treatment by Blei, Kucukelbir and McAuliffe (2017), VI is now the standard scalable inference engine in probabilistic machine learning.