The Millennium Drought (southeastern Australia) provided a natural experiment to challenge
the assumption that watershed streamflow always recovers from drought. Seven years after …

Abstract Hidden Markov models (HMMs) are intensively used in various fields to model and
classify data observed along a line (eg time). The fit of such models strongly relies on the …

The Colorado River basin experienced the worst drought on record during 2000–2004.
Paleoreconstructions of streamflow for the preobservational period show droughts of greater …

Rainfall characteristics at different temporal resolutions play a significant role in sustainable
urban water management. In this study we attempted to identify the temporal characteristics …

In this paper, we consider the filtering and smoothing recursions in nonparametric finite state
space hidden Markov models (HMMs) when the parameters of the model are unknown and …

This paper investigates the properties of Markov switching (MS) models (also known as
hidden Markov models) for generating annual time series. This type of model has been used …

Supplement to “Nonparametric finite translation hidden Markov models and extensions”. In
the supplementary material, we provide an oracle inequality which is used to prove Theorem …

A hierarchical framework for incorporating modes of climate variability into stochastic
simulations of hydrological data is developed, termed the climate‐informed multi‐time scale …

We consider stationary hidden Markov models with finite state space and nonparametric
modeling of the emission distributions. It has remained unknown until very recently that such …

We study the frontier between learnable and unlearnable hidden Markov models (HMMs).
HMMs are flexible tools for clustering dependent data coming from unknown populations …