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We study the impact of sky-based calibration errors from source mismodeling on 21\,cm power spectrum measurements with an interferometer and propose a method for suppressing their effects. While emission from faint sources that are not…

Cosmology and Nongalactic Astrophysics · Physics 2017-07-26 Aaron Ewall-Wice , Joshua S. Dillon , Adrian Liu , Jacqueline Hewitt

In this article we consider static Bayesian parameter estimation for partially observed diffusions that are discretely observed. We work under the assumption that one must resort to discretizing the underlying diffusion process, for…

Computation · Statistics 2017-01-23 Ajay Jasra , Kengo Kamatani , Kody J. H. Law , Yan Zhou

This article addresses the issue of estimating observation parameters (response and error parameters) in inverse problems. The focus is on cases where regularization is introduced in a Bayesian framework and the prior is modeled by a…

Machine Learning · Statistics 2026-02-13 Jean-François Giovannelli

To accurately reproduce measurements from the real world, simulators need to have an adequate model of the physical system and require the parameters of the model be identified. We address the latter problem of estimating parameters through…

Robotics · Computer Science 2022-03-01 Eric Heiden , Christopher E. Denniston , David Millard , Fabio Ramos , Gaurav S. Sukhatme

The current generation of experiments aiming to detect the neutral hydrogen signal from the Epoch of Reionisation (EoR) is likely to be limited by systematic effects associated with removing foreground sources from target fields. In this…

Multi-step forecasting is often described through a simple rule of thumb: recursive strategies are said to have high bias and low variance, while direct strategies are said to have low bias and high variance. We revisit this belief by…

Machine Learning · Computer Science 2025-11-17 Riku Green , Huw Day , Zahraa S. Abdallah , Telmo M. Silva Filho

We investigate solution methods for large-scale inverse problems governed by partial differential equations (PDEs) via Bayesian inference. The Bayesian framework provides a statistical setting to infer uncertain parameters from noisy…

Applications · Statistics 2023-02-08 Mina Karimi , Mehrdad Massoudi , Kaushik Dayal , Matteo Pozzi

Fine particulate matter and aerosol optical thickness are of interest to atmospheric scientists for understanding air quality and its various health/environmental impacts. The available data are extremely large, making uncertainty…

Methodology · Statistics 2025-03-05 Madelyn Clinch , Jonathan R. Bradley

Accurate beam modeling is important in many radio astronomy applications. In this paper, we focus on beam modeling for 21-cm intensity mapping experiments using radio interferometers, though the techniques also apply to single dish…

Instrumentation and Methods for Astrophysics · Physics 2025-09-16 Michael J. Wilensky , Philip Bull , Nicolas Fagnoni

In many geoscientific applications, multiple noisy observations of different origin need to be combined to improve the reconstruction of a common underlying quantity. This naturally leads to multi-parameter models for which adequate…

Numerical Analysis · Mathematics 2015-07-09 C. Gerhards , S. Pereverzyev , P. Tkachenko

Several experimental efforts are underway to measure the power spectrum of 21cm fluctuations from the Epoch of Reionization (EoR) using low-frequency radio interferometers. Experiments like the Hydrogen Epoch of Reionization Array (HERA)…

Instrumentation and Methods for Astrophysics · Physics 2019-06-19 Adam E. Lanman , Jonathan C. Pober

In this article we consider Bayesian estimation of static parameters for a class of partially observed McKean-Vlasov diffusion processes with discrete-time observations over a fixed time interval. This problem features several obstacles to…

Computation · Statistics 2025-04-23 Ajay Jasra , Amin Wu

We apply two methods to estimate the 21~cm bispectrum from data taken within the Epoch of Reionisation (EoR) project of the Murchison Widefield Array (MWA). Using data acquired with the Phase II compact array allows a direct bispectrum…

Deriving Bayesian inference for exponential random graph models (ERGMs) is a challenging "doubly intractable" problem as the normalizing constants of the likelihood and posterior density are both intractable. Markov chain Monte Carlo (MCMC)…

Computation · Statistics 2019-11-26 Linda S. L. Tan , Nial Friel

We quantify the effect of radio frequency interference (RFI) on measurements of the 21-cm power spectrum during the Epoch of Reionization (EoR). Specifically, we investigate how the frequency structure of RFI source emission generates…

Instrumentation and Methods for Astrophysics · Physics 2020-09-09 Michael J. Wilensky , Nichole Barry , Miguel F. Morales , Bryna J. Hazelton , Ruby Byrne

The redshifted 21-cm signal from the epoch of reionization (EoR) directly probes the ionization and thermal states of the intergalactic medium during that period. In particular, the distribution of the ionized regions around the radiating…

The large-scale structure of the Universe should soon be measured at high redshift during the Epoch of Reionization (EoR) through line-intensity mapping. A number of ongoing and planned surveys are using the 21 cm line to trace neutral…

Cosmology and Nongalactic Astrophysics · Physics 2019-05-01 Angus Beane , Francisco Villaescusa-Navarro , Adam Lidz

The Linear Ballistic Accumulator (Brown & Heathcote, 2008) model is used as a measurement tool to answer questions about applied psychology. The analyses based on this model depend upon the model selected and its estimated parameters.…

Methodology · Statistics 2020-03-03 David Gunawan , Guy E. Hawkins , Minh-Ngoc Tran , Robert Kohn , Scott Brown

In recent years dynamical modelling has been provided with a range of breakthrough methods to perform exact Bayesian inference. However it is often computationally unfeasible to apply exact statistical methodologies in the context of large…

Computation · Statistics 2014-12-24 Umberto Picchini , Julie Lyng Forman

Bayesian hierarchical modeling is a popular approach to capturing unobserved heterogeneity across individual units. However, standard estimation methods such as Markov chain Monte Carlo (MCMC) can be impracticable for modeling outcomes from…

Methodology · Statistics 2014-11-04 Michael Braun , Paul Damien