Michael Betancourt

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Stan is a probabilistic programming language for specifying statistical models. A Stan program imperatively defines a log probability function over parameters conditioned on specified data and constants. As of version 2.2.0, Stan provides full Bayesian inference for continuous-variable models through Markov chain Monte Carlo methods such as the No-U-Turn(More)
Leveraging the coherent exploration of Hamil-tonian flow, Hamiltonian Monte Carlo produces computationally efficient Monte Carlo estima-tors, even with respect to complex and high-dimensional target distributions. When confronted with data-intensive applications, however , the algorithm may be too expensive to implement , leaving us to consider the utility(More)
As computational challenges in optimization and statistical inference grow ever harder, algorithms that utilize derivatives are becoming increasingly more important. The implementation of the derivatives that make these algorithms so powerful, however, is a substantial user burden and the practicality of these algorithms depends critically on tools like(More)
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