Michael Betancourt

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  • Bob Carpenter, Daniel Lee, Marcus A Brubaker, Allen Riddell, Andrew Gelman, Ben Goodrich +4 others
  • 2014
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)
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)
Leveraging the coherent exploration of Hamiltonian flow, Hamiltonian Monte Carlo produces computationally efficient Monte Carlo estimators, 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 of(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)
We are sympathetic to the general ideas presented in the article by Pothos & Busemeyer (P&B): Heisenberg's uncertainty principle seems naturally relevant in the social and behavioral sciences, in which measurements can affect the people being studied. We propose that the best approach for developing quantum probability models in the social and behavioral(More)
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