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We study prediction-assimilation systems, which have become routine in meteorology and oceanography and are rapidly spreading to other areas of the geosciences and of continuum physics. The long-term, nonlinear stability of such a system…

Chaotic Dynamics · Physics 2009-11-13 Alberto Carrassi , Michael Ghil , Anna Trevisan , Francesco Uboldi

Conformal prediction provides a distribution-free framework for uncertainty quantification via prediction sets with exact finite-sample coverage. In low dimensions these sets are easy to interpret, but in high-dimensional or structured…

Machine Learning · Statistics 2026-05-08 Trevor Harris

We propose a representation of the Indian summer monsoon rainfall in terms of a probabilistic model based on a Markov Random Field, consisting of discrete state variables representing low and high rainfall at grid-scale and daily rainfall…

Applications · Statistics 2021-01-26 Adway Mitra , Amit Apte , Rama Govindarajan , Vishal Vasan , Sreekar Vadlamani

Precipitation is a large-scale, spatio-temporally heterogeneous phenomenon, with frequent anomalies exhibiting unusually high or low values. We use Markov Random Fields (MRFs) to detect spatio-temporally coherent anomalies in gridded annual…

Applications · Statistics 2017-11-01 Adway Mitra , Ashwin K. Seshadri

We consider the zero-range process with arbitrary bounded monotone rates on the complete graph, in the regime where the number of sites diverges while the density of particles per site converges. We determine the asymptotics of the mixing…

Probability · Mathematics 2018-11-09 Jonathan Hermon , Justin Salez

The statistics of records in sequences of independent, identically distributed random variables is a classic subject of study. One of the earliest results concerns the stochastic independence of record events. Recently, records statistics…

Statistical Mechanics · Physics 2011-09-26 Gregor Wergen , Jasper Franke , Joachim Krug

With the availability of high precision digital sensors and cheap storage medium, it is not uncommon to find large amounts of data collected on almost all measurable attributes, both in nature and man-made habitats. Weather in particular…

Machine Learning · Computer Science 2014-09-18 Bilal Ahmed

The last success problem is an optimal stopping problem that aims to maximize the probability of stopping on the last success in a sequence of independent $n$ Bernoulli trials. In the classical setting where complete information about the…

Probability · Mathematics 2024-07-24 Toru Yoshinaga , Yasushi Kawase

We study Bernoulli bond percolation on a random recursive tree of size $n$ with percolation parameter $p(n)$ converging to $1$ as $n$ tends to infinity. The sizes of the percolation clusters are naturally stored in a tree. We prove…

Probability · Mathematics 2016-12-28 Erich Baur

Remote sensors are becoming the standard for observing and recording ecological data in the field. Such sensors can record data at fine temporal resolutions, and they can operate under extreme conditions prohibitive to human access.…

Artificial Intelligence · Computer Science 2012-06-26 Ethan W. Dereszynski , Thomas G. Dietterich

Regression-based frameworks for streamflow regionalization are built around catchment attributes that traditionally originate from catchment hydrology, flood frequency analysis and their interplay. In this work, we deviated from this…

Methodology · Statistics 2022-06-22 Georgia Papacharalampous , Hristos Tyralis

Let $\{X_n,n\ge1\}$ be a sequence of independent and identically distributed random variables, taking non-negative integer values, and call $X_n$ a $\delta$-record if $X_n>\max\{X_1,...,X_{n-1}\}+\delta$, where $\delta$ is an integer…

Probability · Mathematics 2009-09-29 Raúl Gouet , F. Javier López , Gerardo Sanz

Precipitation exceedance probabilities are widely used in engineering design, risk assessment, and floodplain management. While common approaches like NOAA Atlas 14 assume that extreme precipitation characteristics are stationary over time,…

Applications · Statistics 2025-02-05 Yuchen Lu , Ben Seiyon Lee , James Doss-Gollin

Accurate precipitation forecasting is a vital challenge of societal importance. Though data-driven approaches have emerged as a widely used solution, solely relying on data-driven approaches has limitations in modeling the underlying…

Machine Learning · Computer Science 2024-10-14 Yujin Tang , Jiaming Zhou , Xiang Pan , Zeying Gong , Junwei Liang

In this paper, we propose a nonparametric way to test the hypothesis that time-variation in intraday volatility is caused solely by a deterministic and recurrent diurnal pattern. We assume that noisy high-frequency data from a discretely…

Econometrics · Economics 2026-01-26 Kim Christensen , Ulrich Hounyo , Mark Podolskij

We derive an Ito stochastic differential equation for entropy production in nonequilibrium Langevin processes. Introducing a random-time transformation, entropy production obeys a one-dimensional drift-diffusion equation, independent of the…

Statistical Mechanics · Physics 2017-10-10 Simone Pigolotti , Izaak Neri , Édgar Roldán , Frank Jülicher

Considering the first significant digits (noted d) in data sets of dissipation for turbulent flows, the probability to find a given number (d=1 or 2 or... 9) would be 1/9 for an uniform distribution. Instead the probability closely follows…

Fluid Dynamics · Physics 2015-11-18 Damien Biau

Many West Nile virus (WNV) forecasting frameworks incorporate entomological or avian surveillance data, which may be unavailable in some regions. We introduce a novel data-parsimonious probabilistic model to predict both the timing of…

Applications · Statistics 2025-10-17 Saman Hosseini , Lee W. Cohnstaedt , Matin Marjani , Caterina Scoglio

From infiltration of water into the soil during rainstorms to seasonal plant growth and death, the ecohydrological processes that are thought to be relevant to the formation of banded vegetation patterns in drylands occur across multiple…

Pattern Formation and Solitons · Physics 2020-06-24 Punit Gandhi , Sara Bonetti , Sarah Iams , Amilcare Porporato , Mary Silber

Recent advances in time series research facilitate the development of foundation models. While many state-of-the-art time series foundation models have been introduced, few studies examine their effectiveness in specific downstream…

Machine Learning · Computer Science 2025-12-01 Junyang He , Judy Fox , Alireza Jafari , Ying-Jung Chen , Geoffrey Fox