Statistical signal processing applications usually require the estimation of some parameters
of interest given a set of observed data. These estimates are typically obtained either by …

Forecasts of ecological dynamics in changing environments are increasingly important, and
are available for a plethora of variables, such as species abundance and distribution …

The non-parametric methods have been proposed in the research literature as an
alternative to parametric methods for time series forecasting. However, scarce evidence is …

Compositional multistability is widely observed in multispecies ecological communities.
Since differences in community composition often lead to differences in community function …

Bayesian inference often requires efficient numerical approximation algorithms, such as
sequential Monte Carlo (SMC) and Markov chain Monte Carlo (MCMC) methods. The Gibbs …

Monte Carlo sampling methods are the standard procedure for approximating complicated
integrals of multidimensional posterior distributions in Bayesian inference. In this work, we …

Differential equations have proven to be a powerful mathematical tool in science and
engineering, leading to better understanding, prediction, and control of dynamic processes …

Mapping the network of ecological interactions is a key to understanding the composition,
stability, function and dynamics of microbial communities. In recent years various …

A recent series of papers by Charles T. Perretti and collaborators have shown that
nonparametric forecasting methods can outperform parametric methods in noisy nonlinear …

Ordinary differential equations (ODEs) are foundational in modeling intricate dynamics
across a gamut of scientific disciplines. Yet, a possibility to represent a single phenomenon …