Writing - betanalpha.github.io
Preprints of my research work are posted on the arXiv as much as possible. Highlights include a long but comprehensive introduction to statistical computing and Hamiltonian Monte Carlo targeted at applied researches, and a more theoretical treatment of the geometric foundations of Hamiltonian Monte Carlo.
Preprints of my research work are posted on the arXiv as much as possible. Highlights include a long but comprehensive introduction to statistical computing and Hamiltonian Monte Carlo targeted at applied researches, and a more theoretical treatment of the geometric foundations of Hamiltonian Monte Carlo . I am currently developing a book covering important concepts in probabilistic modeling with Stan . The ultimate goal is a reasonably self-contained treatment demonstrating how to build, evaluate, and utilize probabilistic models that capture domain expertise in applied analyses; the most imp
saved by
related reading
- practicalqmc.pdfartowen.su.domains
- Paperscseweb.ucsd.edu
- uncertain-modern-topics-in-uncertainty-estimation.pdfcalibration-tutorial.github.io
- my projectsdan-iel-lee.vercel.app
- [2506.17366] Gaussian Processes and Reproducing Kernel Hilbert Spaces: Connections and Equivalencesarxiv.org
- course_stat_rl.pdfmit.edu
- Research • Leonid Petrov. Integrable Probabilitylpetrov.cc
- rakhlin_mathstat_sp24.pdfmit.edu
- Betalog · Daniel Littdaniellitt.com
- An optimization perspective on log-concave sampling and beyond | Sinho Chewichewisinho.github.io
- annaabrandenberger.github.io
- Gregory Gundersengregorygundersen.com