14 citations · 37 across the 20 of their papers we have counts for
21 papers
Dynamic survival analysis: modelling the hazard function via ordinary differential equations
J. A. Christen, F. J. Rubio
The hazard function represents one of the main quantities of interest in the analysis of survival data. We propose a general approach for parametrically modelling the dynamics of t…
A surrogate model for studying random field energy release rates in 2D brittle fractures
Luis Blanco-Cocom, Marcos A. Capistrán, Jaroslaw Knap +1
This article proposes a weighted-variational model as an approximated surrogate model to lessen numerical complexity and lower the execution times of brittle fracture simulations.…
Bayesian sequential data assimilation for COVID-19 forecasting
Maria L. Daza-Torres, Marcos A. Capistrán, Antonio Capella +1
We introduce a Bayesian sequential data assimilation method for COVID-19 forecasting. It is assumed that suitable transmission, epidemic and observation models are available and pr…
A simulation study to compare 210Pb dating data analyses
Marco A Aquino-López, Nicole K. Sanderson, Maarten Blaauw +4
The increasing interest in understanding anthropogenic impacts on the environment have led to a considerable number of studies focusing on sedimentary records for the last 1…
Filtering and improved Uncertainty Quantification in the dynamic estimation of effective reproduction numbers
Marcos A. Capistrán, Antonio Capella, J. Andrés Christen
The effective reproduction number measures an infectious disease's transmissibility as the number of secondary infections in one reproduction time in a population having both…
Uncertainty quantification for fault slip inversion
J. Cricelio Montesinos-López, Antonio Capella, J. Andrés Christen +1
We propose an efficient Bayesian approach to infer a fault displacement from geodetic data in a slow slip event. Our physical model of the slip process reduces to a multiple linear…
Forecasting hospital demand during COVID-19 pandemic outbreaks
Marcos A. Capistran, Antonio Capella, J. Andres Christen
We present a compartmental SEIRD model aimed at forecasting hospital occupancy in metropolitan areas during the current COVID-19 outbreak. The model features asymptomatic and sympt…
Systematic statistical analysis of microbial data from dilution series
J Andrés Christen, Al Parker
In microbial studies, samples are often treated under different experimental conditions and then tested for microbial survival. A technique, dating back to the 1880's, consists of…
Error control in the numerical posterior distribution in the Bayesian UQ analysis of a semilinear evolution PDE
Maria L. Daza-Torres, J. Cricelio Montesinos-López, Marcos A. Capistrán +2
We elaborate on results obtained in \cite{christen2018} for controlling the numerical posterior error for Bayesian UQ problems, now considering forward maps arising from the soluti…
FATSO: A family of operators for variable selection in linear models
Nicolás E. Kuschinski, J. Andrés Christen
In linear models it is common to have situations where several regression coefficients are zero. In these situations a common tool to perform regression is a variable selection ope…
Bayesian Experimental Design for Oral Glucose Tolerance Tests (OGTT)
Nicolás E. Kuschinski, J. Andrés Christen, Adriana Monroy +1
OGTT is a common test, frequently used to diagnose insulin resistance or diabetes, in which a patient's blood sugar is measured at various times over the course of a few hours. Rec…
A computational geometry method for the inverse scattering problem
Maria L. Daza-Torres, Juan Antonio Infante del Río, Marcos A. Capistrán +1
In this paper we demonstrate a computational method to solve the inverse scattering problem for a star-shaped, smooth, penetrable obstacle in 2D. Our method is based on classical i…
Posterior distribution existence and error control in Banach spaces in the Bayesian approach to UQ in inverse problems
J. Andrés Christen, Marcos A. Capistrán, M. Luisa Daza-Torres +2
We generalize the results of \cite{Capistran2016} on expected Bayes factors (BF) to control the numerical error in the posterior distribution to an infinite dimensional setting whe…
Sampling hyperparameters in hierarchical models: improving on Gibbs for high-dimensional latent fields and large data sets
Richard A. Norton, J. Andres Christen, Colin Fox
We consider posterior sampling in the very common Bayesian hierarchical model in which observed data depends on high-dimensional latent variables that, in turn, depend on relativel…
Numerical posterior distribution error control and expected Bayes Factors in the bayesian Uncertainty Quantification of Inverse Problems
J. Andrés Christen, Marcos A. Capistrán, Miguel Ángel Moreles
In the bayesian analysis of Inverse Problems most relevant cases the forward maps (FM, or regressor function) are defined in terms of a system of (O, P)DE's with intractable soluti…
Modeling Oral Glucose Tolerance Test (OGTT) data and its Bayesian Inverse Problem
Nicolás Kuschinski, J. Andrés Christen, Adriana Monroy +1
One common way to test for diabetes is the Oral Glucose Tolerance Test or OGTT. Most common methods for the analysis of the data on this test are wasteful of much of the informatio…
An analysis of the interaction between influenza and respiratory syncytial virus based on acute respiratory infection records
Yendry N. Arguedas-Flatts, Marcos A. Capistrán, J. Andrés Christen +1
Under the hypothesis that both influenza and respiratory syncytial virus (RSV) are the two leading causes of acute respiratory infections (ARI), in this paper we have used a standa…
Bayesian Analysis of ODE's: solver optimal accuracy and Bayes factors
Marcos Capistrán, J. Andrés Christen, Sophie Donnet
In most relevant cases in the Bayesian analysis of ODE inverse problems, a numerical solver needs to be used. Therefore, we cannot work with the exact theoretical posterior distrib…
On optimal direction gibbs sampling
J. Andrés Christen, Colin Fox, Diego Andrés Pérez-Ruiz +1
Generalized Gibbs kernels are those that may take any direction not necessarily bounded to each axis along the parameters of the objective function. We study how to optimally choos…
Towards Uncertainty Quantification and Inference in the stochastic SIR Epidemic Model
Marcos A. Capistrán, J. Andrés Christen, Jorge X. Velasco-Hernández
In this paper we introduce a novel method to conduct inference with models defined through a continuous-time Markov process, and we apply these results to a classical stochastic SI…
A Generic Multivariate Distribution for Counting Data
Marcos Capistrán, J. Andrés Christen
Motivated by the need, in some Bayesian likelihood free inference problems, of imputing a multivariate counting distribution based on its vector of means and variance-covariance ma…