Data Science Sydney Michael Betancourt
Автор: DataScienceSydney
Загружено: 2019-03-01
Просмотров: 3248
SCALABLE BAYESIAN INFERENCE WITH HAMILTONIAN MONTE CARLO - the promise of big data, inferences are often limited not by sample size but rather by systematic effects. Only by carefully modeling these effects can we take full advantage of the data -- big data must be complemented with big models and the algorithms that can fit them. One such algorithm is Hamiltonian Monte Carlo, which exploits the inherent geometry of the posterior distribution to admit full Bayesian inference that scales to the complex models of practical interest. In this talk I will present a conceptual discussion of the challenges inherent to Bayesian computation and the foundations of why Hamiltonian Monte Carlo in uniquely suited to surmount them.
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