7 citations · 21 across the 12 of their papers we have counts for
12 papers
Geometric Ergodicity in Modified Variations of Riemannian Manifold and Lagrangian Monte Carlo
James A. Brofos, Vivekananda Roy, Roy R. Lederman
Riemannian manifold Hamiltonian (RMHMC) and Lagrangian Monte Carlo (LMC) have emerged as powerful methods of Bayesian inference. Unlike Euclidean Hamiltonian Monte Carlo (EHMC) and…
Several Remarks on the Numerical Integrator in Lagrangian Monte Carlo
James A. Brofos, Roy R. Lederman
Riemannian manifold Hamiltonian Monte Carlo (RMHMC) is a powerful method of Bayesian inference that exploits underlying geometric information of the posterior distribution in order…
On Numerical Considerations for Riemannian Manifold Hamiltonian Monte Carlo
James A. Brofos, Roy R. Lederman
Riemannian manifold Hamiltonian Monte Carlo (RMHMC) is a sampling algorithm that seeks to adapt proposals to the local geometry of the posterior distribution. The specific form of…
Adaptation of the Independent Metropolis-Hastings Sampler with Normalizing Flow Proposals
James A. Brofos, Marylou Gabrié, Marcus A. Brubaker +1
Markov Chain Monte Carlo (MCMC) methods are a powerful tool for computation with complex probability distributions. However the performance of such methods is critically dependant…
Manifold Density Estimation via Generalized Dequantization
James A. Brofos, Marcus A. Brubaker, Roy R. Lederman
Density estimation is an important technique for characterizing distributions given observations. Much existing research on density estimation has focused on cases wherein the data…
Evaluating the Implicit Midpoint Integrator for Riemannian Manifold Hamiltonian Monte Carlo
James A. Brofos, Roy R. Lederman
Riemannian manifold Hamiltonian Monte Carlo is traditionally carried out using the generalized leapfrog integrator. However, this integrator is not the only choice and other integr…
Magnetic Manifold Hamiltonian Monte Carlo
James A. Brofos, Roy R. Lederman
Markov chain Monte Carlo (MCMC) algorithms offer various strategies for sampling; the Hamiltonian Monte Carlo (HMC) family of samplers are MCMC algorithms which often exhibit impro…
Non-Canonical Hamiltonian Monte Carlo
James A. Brofos, Roy R. Lederman
Hamiltonian Monte Carlo is typically based on the assumption of an underlying canonical symplectic structure. Numerical integrators designed for the canonical structure are incompa…
A Markov Chain Analysis of a Pattern Matching Coin Game
James Brofos
In late May of 2014 I received an email from a colleague introducing to me a non-transitive game developed by Walter Penney. This paper explores this probability game from the pers…
A Hybrid Monte Carlo Architecture for Parameter Optimization
James Brofos
Much recent research has been conducted in the area of Bayesian learning, particularly with regard to the optimization of hyper-parameters via Gaussian process regression. The meth…
Automated Attribution and Intertextual Analysis
James Brofos, Ajay Kannan, Rui Shu
In this work, we employ quantitative methods from the realm of statistics and machine learning to develop novel methodologies for author attribution and textual analysis. In partic…
Ensemble Committees for Stock Return Classification and Prediction
James Brofos
This paper considers a portfolio trading strategy formulated by algorithms in the field of machine learning. The profitability of the strategy is measured by the algorithm's capabi…