5 citations · 11 across the 9 of their papers we have counts for
9 papers
Computationally efficient segmentation for non-stationary time series with oscillatory patterns
Nicolas Bianco, Lorenzo Cappello
We propose a novel approach for change-point detection and parameter learning in multivariate non-stationary time series exhibiting oscillatory behaviour. We approximate the proces…
A Bayesian framework for change-point detection with uncertainty quantification
Davis Berlind, Lorenzo Cappello, Oscar Hernan Madrid Padilla
We introduce a novel Bayesian method that can detect multiple structural breaks in the mean and variance of a length time-series. Our method quantifies uncertainty by returning…
Bayesian Predictive Inference Beyond Martingales
Marco Battiston, Lorenzo Cappello
There is a growing interest in the so-called Bayesian Predictive Inference approach, which allows to perform Bayesian inference without specifying the likelihood and prior of the m…
Bayesian variance change point detection with credible sets
Lorenzo Cappello, Oscar Hernan Madrid Padilla
This paper introduces a novel Bayesian approach to detect changes in the variance of a Gaussian sequence model, focusing on quantifying the uncertainty in the change point location…
Statistical Challenges in Tracking the Evolution of SARS-CoV-2
Lorenzo Cappello, Jaehee Kim, Sifan Liu +1
Genomic surveillance of SARS-CoV-2 has been instrumental in tracking the spread and evolution of the virus during the pandemic. The availability of SARS-CoV-2 molecular sequences i…
Scalable Bayesian change point detection with spike and slab priors
Lorenzo Cappello, Oscar Hernan Madrid Padilla, Julia A. Palacios
We study the use of spike and slab priors for consistent estimation of the number of change points and their locations. Leveraging recent results in the variable selection literatu…
Adaptive preferential sampling in phylodynamics
Lorenzo Cappello, Julia A. Palacios
Longitudinal molecular data of rapidly evolving viruses and pathogens provide information about disease spread and complement traditional surveillance approaches based on case coun…
The Tajima heterochronous n-coalescent: inference from heterochronously sampled molecular data
Lorenzo Cappello, Amandine Veber, Julia A. Palacios
The observed sequence variation at a locus informs about the evolutionary history of the sample and past population size dynamics. The Kingman coalescent is used in a generative mo…
Sequential importance sampling for multi-resolution Kingman-Tajima coalescent counting
Lorenzo Cappello, Julia A. Palacios
Statistical inference of evolutionary parameters from molecular sequence data relies on coalescent models to account for the shared genealogical ancestry of the samples. However, i…