57 citations · 159 across the 36 of their papers we have counts for
5 papers · 1 filter
A Bayesian approach for fitting semi-Markov mixture models of cancer latency to individual-level data
Raphael Morsomme, Shannon Holloway, Marc Ryser +1
Multi-state models of cancer natural history are widely used for designing and evaluating cancer early detection strategies. Calibrating such models against longitudinal data from…
Fast Computation of Branching Process Transition Probabilities via ADMM
Achal Awasthi, Jason Xu
Branching processes are a class of continuous-time Markov chains (CTMCs) prevalent for modeling stochastic population dynamics in ecology, biology, epidemiology, and many other fie…
Exact Inference for Stochastic Epidemic Models via Uniformly Ergodic Block Sampling
Raphael Morsomme, Jason Xu
Stochastic epidemic models provide an interpretable probabilistic description of the spread of a disease through a population. Yet, fitting these models to partially observed data…
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…
Efficient Transition Probability Computation for Continuous-Time Branching Processes via Compressed Sensing
Jason Xu, Vladimir N. Minin
Branching processes are a class of continuous-time Markov chains (CTMCs) with ubiquitous applications. A general difficulty in statistical inference under partially observed CTMC m…