38 citations · 68 across the 11 of their papers we have counts for
12 papers
Joint Neural Posterior Estimation and Model Selection for Chemical Reaction Networks
J. Arturo Esquivel, Jorge A. Campos-Gonzalez-Angulo, Sergio Pablo-García +2
Simulation-based Bayesian inference for Chemical Reaction Networks (CRNs) must simultaneously solve two computationally demanding problems: discriminating between overlapping react…
Scalable Bayesian Additive Models for Stellar Flare Detection via Amortized Gaussian Process Inference and Hidden Markov Models
Rodrigo Herrera, Vianey Leos-Barajas, Gwendolyn Eadie +2
Gaussian Processes (GPs) are a powerful tool for Bayesian time-series modeling, yet their cubic computational cost remains a severe barrier for application to long, high-cadence da…
Bayesian inference for hidden Markov models under genuine multimodality with application to ecological time series
Marco A. Gallegos-Herrada, Vianey Leos-Barajas, Jeffrey S. Rosenthal
Bayesian inference in hidden Markov models (HMMs) can be challenging due to the presence of multimodality in the likelihood function, and consequently in the joint posterior distri…
Detecting stellar flares in photometric data using hidden Markov models
J. Arturo Esquivel, Yunyi Shen, Vianey Leos-Barajas +5
We present a hidden Markov model (HMM) for discovering stellar flares in light curve data of stars. HMMs provide a framework to model time series data that are not stationary; they…
Copula Modelling of Serially Correlated Multivariate Data with Hidden Structures
Robert Zimmerman, Radu V. Craiu, Vianey Leos-Barajas
We propose a copula-based extension of the hidden Markov model (HMM) which applies when the observations recorded at each time in the sample are multivariate. The joint model produ…
Hidden Markov and semi-Markov models: When and why are these models useful for classifying states in time series data?
Sofia Ruiz-Suarez, Vianey Leos-Barajas, Juan Manuel Morales
Hidden Markov models (HMMs) and their extensions have proven to be powerful tools for classification of observations that stem from systems with temporal dependence as they take in…
The Discrete Adjoint Method: Efficient Derivatives for Functions of Discrete Sequences
Michael Betancourt, Charles C. Margossian, Vianey Leos-Barajas
Gradient-based techniques are becoming increasingly critical in quantitative fields, notably in statistics and computer science. The utility of these techniques, however, ultimatel…
Approximate Bayesian inference for a "steps and turns" continuous-time random walk observed at regular time intervals
Sofia Ruiz-Suarez, Vianey Leos-Barajas, Ignacio Alvarez-Castro +1
The study of animal movement is challenging because it is a process modulated by many factors acting at different spatial and temporal scales. Several models have been proposed whi…
An Introduction to Animal Movement Modeling with Hidden Markov Models using Stan for Bayesian Inference
Vianey Leos-Barajas, Théo Michelot
Hidden Markov models (HMMs) are popular time series model in many fields including ecology, economics and genetics. HMMs can be defined over discrete or continuous time, though her…
The Hot Hand in Professional Darts
Marius Ötting, Roland Langrock, Christian Deutscher +1
We investigate the hot hand hypothesis in professional darts in a near-ideal setting with minimal to no interaction between players. Considering almost one year of tournament data,…
Multi-scale modeling of animal movement and general behavior data using hidden Markov models with hierarchical structures
Vianey Leos-Barajas, Eric Gangloff, Timo Adam +4
Hidden Markov models (HMMs) are commonly used to model animal movement data and infer aspects of animal behavior. An HMM assumes that each data point from a time series of observat…
Analysis of animal accelerometer data using hidden Markov models
Vianey Leos-Barajas, Theoni Photopoulou, Roland Langrock +4
Use of accelerometers is now widespread within animal biotelemetry as they provide a means of measuring an animal's activity in a meaningful and quantitative way where direct obser…