469 citations · 2k across the 43 of their papers we have counts for
13 papers · 1 filter
Granger Causality: A Review and Recent Advances
Ali Shojaie, Emily B. Fox
Introduced more than a half century ago, Granger causality has become a popular tool for analyzing time series data in many application domains, from economics and finance to genom…
Granger Causality Networks for Categorical Time Series
Alex Tank, Emily B. Fox, Ali Shojaie
We present a new framework for learning Granger causality networks for multivariate categorical time series, based on the mixture transition distribution (MTD) model. Traditionally…
Identifiability and Estimation of Structural Vector Autoregressive Models for Subsampled and Mixed Frequency Time Series
Alex Tank, Emily B. Fox, Ali Shojaie
Causal inference in multivariate time series is challenging due to the fact that the sampling rate may not be as fast as the timescale of the causal interactions. In this context,…
Irreversible Samplers from Jump and Continuous Markov Processes
Yi-An Ma, Emily B. Fox, Tianqi Chen +1
In this paper, we propose irreversible versions of the Metropolis Hastings (MH) and Metropolis adjusted Langevin algorithm (MALA) with a main focus on the latter. For the former, w…
Bayesian Structure Learning for Stationary Time Series
Alex Tank, Nicholas Foti, Emily Fox
While much work has explored probabilistic graphical models for independent data, less attention has been paid to time series. The goal in this setting is to determine conditional…
Stochastic Gradient Hamiltonian Monte Carlo
Tianqi Chen, Emily B. Fox, Carlos Guestrin
Hamiltonian Monte Carlo (HMC) sampling methods provide a mechanism for defining distant proposals with high acceptance probabilities in a Metropolis-Hastings framework, enabling mo…