6 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- University of California, Los AngelesUS3 papers
- North Carolina State UniversityUS2 papers
- University of WashingtonUS2 papers
- Yale UniversityUS2 papers
- Biomathematics and Statistics ScotlandGB1 paper
- Foxconn (Taiwan)TW1 paper
- KU LeuvenBE1 paper
- Michigan State UniversityUS1 paper
- National Taiwan UniversityTW1 paper
- Rega Institute for Medical ResearchBE1 paper
- Tata Institute of Fundamental ResearchIN1 paper
- University of ArizonaUS1 paper
6 papers
Quantum Speedups for Multiproposal MCMC
Chin-Yi Lin, Kuo-Chin Chen, Philippe Lemey +3
Multiproposal Markov chain Monte Carlo (MCMC) algorithms choose from multiple proposals to generate their next chain step in order to sample from challenging target distributions m…
Structural Risk Minimization for Regression
Adam Gustafson, Matthew Hirn, Kitty Mohammed +2
One means of fitting functions to high-dimensional data is by providing smoothness constraints. Recently, the following smooth function approximation problem was proposed: given a…
Birth/birth-death processes and their computable transition probabilities with biological applications
Lam Si Tung Ho, Jason Xu, Forrest W. Crawford +2
Birth-death processes track the size of a univariate population, but many biological systems involve interaction between populations, necessitating models for two or more populatio…
MM Algorithms for Variance Components Models
Hua Zhou, Liuyi Hu, Jin Zhou +1
Variance components estimation and mixed model analysis are central themes in statistics with applications in numerous scientific disciplines. Despite the best efforts of generatio…
Birth-death processes
Forrest W. Crawford, Marc A. Suchard
Many important stochastic counting models can be written as general birth-death processes (BDPs). BDPs are continuous-time Markov chains on the non-negative integers and can be use…
Path Following in the Exact Penalty Method of Convex Programming
Hua Zhou, Kenneth Lange
Classical penalty methods solve a sequence of unconstrained problems that put greater and greater stress on meeting the constraints. In the limit as the penalty constant tends to $…