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Uncertainty is critical to reliable decision-making with machine learning. Conformal prediction (CP) handles uncertainty by predicting a set on a test input, hoping the set to cover the true label with at least $(1-\alpha)$ confidence. This…

Machine Learning · Computer Science 2024-03-25 Rui Xu , Yue Sun , Chao Chen , Parv Venkitasubramaniam , Sihong Xie

We present exact non-Gaussian joint likelihoods for auto- and cross-correlation functions on arbitrarily masked spherical Gaussian random fields. Our considerations apply to spin-0 as well as spin-2 fields but are demonstrated here for the…

Cosmology and Nongalactic Astrophysics · Physics 2025-09-11 Veronika Oehl , Tilman Tröster

Every probability distribution can be approximated up to a given precision by a phase-type distribution, i.e. a distribution encoded by a continuous time Markov chain (CTMC). However, an excessive number of states in the corresponding CTMC…

Performance · Computer Science 2014-07-01 Ľuboš Korenčiak , Jan Krčál , Vojtěch Řehák

This paper is an overview of the classical level crossing problem which is studied extensively in the literature and is fundamental in many branches of applied probability. We discuss a number of approximations with an emphasis on their…

Probability · Mathematics 2018-03-28 Vsevolod Malinovskii

We study the 2-dimensional Ising model at critical temperature on a simply connected subset $\Omega_{\delta}$ of the square grid $\delta\mathbb{Z}^{2}$. The scaling limit of the critical Ising model is conjectured to be described by…

Mathematical Physics · Physics 2018-11-26 Reza Gheissari , Clément Hongler , S. C. Park

We introduce Conformal Interquantile Regression (CIR), a conformal regression method that efficiently constructs near-minimal prediction intervals with guaranteed coverage. CIR leverages black-box machine learning models to estimate outcome…

Machine Learning · Statistics 2026-01-07 Naixin Guo , Rui Luo , Zhixin Zhou

Mixtures of Linear Regressions (MLR) is an important mixture model with many applications. In this model, each observation is generated from one of the several unknown linear regression components, where the identity of the generated…

Machine Learning · Computer Science 2020-03-31 Yuanzhi Li , Yingyu Liang

In this paper, we introduce a novel numerical approach for approximating the SIR model in epidemiology. Our method enhances the existing linearization procedure by incorporating a suitable relaxation term to tackle the transcendental…

Numerical Analysis · Mathematics 2023-08-17 Vo Anh Khoa , Pham Minh Quan , Ja'Niyah Allen , Kbenesh W. Blayneh

This article presents a new class of generalized transmuted lifetime distributions which includes a large number of lifetime distributions as sub-family. Several important mathematical quantities such as density function, distribution…

Methodology · Statistics 2024-05-21 Alok Kumar Pandey , Alam Ali , Ashok Kumar Pathak

In response to the comment made by Dr. Shirakura {\it et al} (cond-mat/0011235), we explain that their scaling forms of the order parameter distribution are inadequate. We then present an appropriate scaling form of the order parameter…

Disordered Systems and Neural Networks · Physics 2007-05-23 H. Kitatani , A. Sinada

The Girsanov transform and Kolmogorov equations are two useful methods for studying SPDEs. It is shown that, under suitable conditions, the series expansion obtained from the Girsanov transform coincides with the one generated by an…

Probability · Mathematics 2022-11-22 Franco Flandoli , Dejun Luo , Cristiano Ricci

We study the special class of the exact solutions in cosmological models based on the Generalized Scalar-Tensor Gravity with non-minimal coupling of a scalar field to the Ricci scalar and to the Gauss-Bonnet scalar in 4D Friedmann universe…

General Relativity and Quantum Cosmology · Physics 2019-07-17 Igor V. Fomin , Sergey V. Chervon

We propose the Cyclic Permutation Test (CPT) to test general linear hypotheses for linear models. This test is non-randomized and valid in finite samples with exact Type I error $\alpha$ for an arbitrary fixed design matrix and arbitrary…

Methodology · Statistics 2021-01-01 Lihua Lei , Peter J. Bickel

The SIR model is a three-compartment model of the time development of an epidemic. After normalizing the dependent variables, the model is a system of two non-linear differential equations for the susceptible proportion $S$ and the infected…

Dynamical Systems · Mathematics 2021-04-27 William G. Faris

The partial correlation graphical LASSO (PCGLASSO) is a penalised likelihood method for Gaussian graphical models which provides scale invariant sparse estimation of the precision matrix and improves upon the popular graphical LASSO method.…

Methodology · Statistics 2025-10-30 Jack Storror Carter , Cesare Molinari

An explicit weak solution for the 3/2 stochastic volatility model is obtained and used to develop a simulation algorithm for option pricing purposes. The 3/2 model is a non-affine stochastic volatility model whose variance process is the…

Computational Finance · Quantitative Finance 2021-01-12 Iro René Kouarfate , Michael A. Kouritzin , Anne MacKay

A generalised integer S Ising spin glass model is analysed using the replica formalism. The bilinear couplings are assumed to have a Gaussian distribution with ferromagnetic mean <J_ij> = Jo. Incorporation of a quadrupolar interaction term…

Statistical Mechanics · Physics 2009-10-28 G. R. Schreiber

The Smoluchowski equation with a time dependent sink term is solved exactly. In this method by knowing the probability distribution at the origin P(0,s), one may derive the probability distribution at all positions i.e., P(x,s). Further the…

Quantum Physics · Physics 2015-06-01 Diwaker , Anirudhha Chakraborty

We consider probabilistic model checking for continuous-time Markov chains (CTMCs) induced from Stochastic Reaction Networks (SRNs) against a fragment of Continuous Stochastic Logic (CSL) extended with reward operators. Classical numerical…

Logic in Computer Science · Computer Science 2018-04-25 Luca Bortolussi , Luca Cardelli , Marta Kwiatkowska , Luca Laurenti

Much of the recent work on random constraint satisfaction problems has been inspired by ingenious but non-rigorous approaches from physics. The physics predictions typically come in the form of distributional fixed point problems that are…

Probability · Mathematics 2015-10-08 Victor Bapst , Amin Coja-Oghlan