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Found 9 papers, 6 papers with code
A Unifying and Canonical Description of Measure-Preserving Diffusions
A complete recipe of measure-preserving diffusions in Euclidean space was recently derived unifying several MCMC algorithms into a single framework.
Bregman dynamics, contact transformations and convex optimization
Recent research on accelerated gradient methods of use in optimization has demonstrated that these methods can be derived as discretizations of dynamical systems.
Validating Bayesian Inference Algorithms with Simulation-Based Calibration
Verifying the correctness of Bayesian computation is challenging.
Methodology
Visualization in Bayesian workflow
Bayesian data analysis is about more than just computing a posterior distribution, and Bayesian visualization is about more than trace plots of Markov chains.
Methodology Applications
A Conceptual Introduction to Hamiltonian Monte Carlo
Hamiltonian Monte Carlo has proven a remarkable empirical success, but only recently have we begun to develop a rigorous under- standing of why it performs so well on difficult problems and how it is best applied in practice.
Methodology
Diagnosing Suboptimal Cotangent Disintegrations in Hamiltonian Monte Carlo
When properly tuned, Hamiltonian Monte Carlo scales to some of the most challenging high-dimensional problems at the frontiers of applied statistics, but when that tuning is suboptimal the performance leaves much to be desired.
Methodology
On the Geometric Ergodicity of Hamiltonian Monte Carlo
We establish general conditions under which Markov chains produced by the Hamiltonian Monte Carlo method will and will not be geometrically ergodic.
Identifying the Optimal Integration Time in Hamiltonian Monte Carlo
By leveraging the natural geometry of a smooth probabilistic system, Hamiltonian Monte Carlo yields computationally efficient Markov Chain Monte Carlo estimation.
Methodology Computation
The Stan Math Library: Reverse-Mode Automatic Differentiation in C++
As computational challenges in optimization and statistical inference grow ever harder, algorithms that utilize derivatives are becoming increasingly more important.
Mathematical Software G.1.0; G.1.3; G.1.4; F.2.1
