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Articles 1 - 6 of 6
Full-Text Articles in Physical Sciences and Mathematics
Arma Model Development And Analysis For Global Temperature Uncertainty, Mahmud Hasan, Gauree Wathodkar, Mathias Muia
Arma Model Development And Analysis For Global Temperature Uncertainty, Mahmud Hasan, Gauree Wathodkar, Mathias Muia
Faculty and Student Publications
Temperature uncertainty models for land and sea surfaces can be developed based on statistical methods. In this paper, we developed a novel time-series temperature uncertainty model, which is the autoregressive moving average (ARMA) (1,1) model. The model was developed for an observed annual mean temperature anomaly X(t), which is a combination of a true (latent) global anomaly Y(t) for a year (t) and normal variable w(t). The uncertainty is taken as the variance of w(t), which was divided into land surface temperature (LST) uncertainty, sea surface temperature (SST) uncertainty, and the corresponding source of uncertainty. The ARMA …
On Some Mixing Properties Of Copula-Based Markov Chains, Martial Longla, Hamadou Mous-Abou, Isidore Seraphin Ngongo
On Some Mixing Properties Of Copula-Based Markov Chains, Martial Longla, Hamadou Mous-Abou, Isidore Seraphin Ngongo
Faculty and Student Publications
This paper brings some insights of ψ′-mixing, ψ∗-mixing and ψ-mixing for copula-based Markov chains and the perturbations of their copulas. We provide new tools to check Markov chains for ψ-mixing or ψ′-mixing. We show that perturbations of ψ′-mixing copula-based Markov chains are ψ′-mixing while perturbations of ψ-mixing Markov chains are not necessarily ψ-mixing Markov chains, even when the perturbed copula generates ψ-mixing. The Farlie–Gumbel–Morgenstern, gaussian and Ali-Mikhail-Haq copula families are considered among other examples. A statistical study is provided to emphasize the impact of perturbations on copula-based Markov chains in a simulation study. Moreover, we provide a correction to a …
Beta Invariant And Chromatic Uniqueness Of Wheels, Sooyeon Lee, Haidong Wu
Beta Invariant And Chromatic Uniqueness Of Wheels, Sooyeon Lee, Haidong Wu
Faculty and Student Publications
A graph G is chromatically unique if its chromatic polynomial completely determines the graph. An n-spoked wheel, Wn, is shown to be chromatically unique when n ≥ 4 is even [S.-J. Xu and N.-Z. Li, The chromaticity of wheels, Discrete Math. 51 (1984) 207–212]. When n is odd, this problem is still open for n ≥ 15 since 1984, although it was shown by di erent researchers that the answer is no for n = 5, 7, yes for n = 3, 9, 11, 13, and unknown for other odd n. We use the beta invariant of matroids to prove …
Sequential Monte Carlo Methods In The Nimble And Nimblesmc R Packages, Nicholas Michaud, Perry De Valpine, Daniel Turek, Dao Nguyen
Sequential Monte Carlo Methods In The Nimble And Nimblesmc R Packages, Nicholas Michaud, Perry De Valpine, Daniel Turek, Dao Nguyen
Faculty and Student Publications
nimble is an R package for constructing algorithms and conducting inference on hierarchical models. The nimble package provides a unique combination of flexible model specification and the ability to program model-generic algorithms. Specifically, the package allows users to code models in the BUGS language, and it allows users to write algorithms that can be applied to any appropriate model. In this paper, we introduce the nimbleSMC R package. nimbleSMC contains algorithms for state-space model analysis using sequential Monte Carlo (SMC) techniques that are built using nimble. We first provide an overview of state-space models and commonly-used SMC algorithms. We then …
Nested Adaptation Of Mcmc Algorithms, Dao Nguyen, Perry De Valpine, Yves Atchade, Daniel Turek, Nicholas Michaud, Christopher Paciorek
Nested Adaptation Of Mcmc Algorithms, Dao Nguyen, Perry De Valpine, Yves Atchade, Daniel Turek, Nicholas Michaud, Christopher Paciorek
Faculty and Student Publications
© 2020 International Society for Bayesian Analysis Markov chain Monte Carlo (MCMC) methods are ubiquitous tools for simulation-based inference in many fields but designing and identifying good MCMC samplers is still an open question. This paper introduces a novel MCMC algorithm, namely, Nested Adaptation MCMC. For sampling variables or blocks of variables, we use two levels of adaptation where the inner adaptation optimizes the MCMC performance within each sampler, while the outer adaptation explores the space of valid kernels to find the optimal samplers. We provide a theoretical foundation for our approach. To show the generality and usefulness of the …
Automating The Calculation Of The Hilbert–Kunz Multiplicity And F-Signature, Gabriel Johnson, Sandra Spiroff
Automating The Calculation Of The Hilbert–Kunz Multiplicity And F-Signature, Gabriel Johnson, Sandra Spiroff
Faculty and Student Publications
© 2018 The Authors The Hilbert–Kunz multiplicity and F-signature are important invariants for researchers in commutative algebra and algebraic geometry. We provide software, and describe the automation, for the calculations of the two invariants in the case of intersection algebras over polynomial rings.