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Because of the high eccentricities (~0.3) of two of the possible planets about the star Upsilon Andromeda, the stability of the system requires careful study. We present results of 1000 numerical simulations which explore the orbital…

Astrophysics · Physics 2009-10-31 Rory Barnes , Thomas Quinn

Assessing uncertainty is an important step towards ensuring the safety and reliability of machine learning systems. Existing uncertainty estimation techniques may fail when their modeling assumptions are not met, e.g. when the data…

Machine Learning · Computer Science 2017-01-24 Volodymyr Kuleshov , Stefano Ermon

Peak estimation bounds extreme values of a function of state along trajectories of a dynamical system. This paper focuses on extending peak estimation to continuous and discrete settings with time-independent and time-dependent uncertainty.…

Optimization and Control · Mathematics 2021-03-25 Jared Miller , Didier Henrion , Mario Sznaier , Milan Korda

Empirical science needs to be based on facts and claims that can be reproduced. This calls for replicating the studies that proclaim the claims, but practice in most fields still fails to implement this idea. When such studies emerged in…

Other Statistics · Statistics 2025-08-27 Werner A. Stahel

What is the origin of macroscopic randomness (uncertainty)? This is one of the most fundamental open questions for human being. In this paper, 10000 samples of reliable (convergent), multiple-scale (from 1.0E-60 to 100) numerical…

Chaotic Dynamics · Physics 2015-09-09 Shijun Liao , Xiaoming Li

Applying standard statistical methods after model selection may yield inefficient estimators and hypothesis tests that fail to achieve nominal type-I error rates. The main issue is the fact that the post-selection distribution of the data…

Methodology · Statistics 2019-05-23 Amit Meir , Mathias Drton

Lord's Paradox occurs when a continuous covariate is statistically controlled for and the relationship between a continuous outcome and group status indicator changes in both magnitude and direction. This phenomenon poses a challenge to the…

Methodology · Statistics 2017-08-15 ZhiMin Xiao , Steve Higgins , Adetayo Kasim

Suppose $\widehat\theta_n$ is a strongly consistent estimator for $\theta_0$ in some i.i.d. situation. Let $N_\varepsilon$ and $Q_\varepsilon$ be respectively the last $n$ and the total number of $n$ for which $\widehat\theta_n$ is at least…

Statistics Theory · Mathematics 2026-03-11 Nils Lid Hjort , Grete Fenstad

In this paper, we will see that the proportion of d as leading digit, d $\in$ 1, 9, in data (obtained thanks to the hereunder developed model) is more likely to follow a law whose probability distribution is determined by a specific upper…

Probability · Mathematics 2018-06-13 Stéphane Blondeau da Silva

Results for higher-order threshold enhancements in high-E_T jet production in hadron-hadron collisions are presented. Expressions are given for the next-to-next-to-leading order (NNLO) threshold corrections to the single-jet inclusive cross…

High Energy Physics - Phenomenology · Physics 2009-10-31 Nikolaos Kidonakis , J. F. Owens

For many years, the intuitions underlying partial-order planning were largely taken for granted. Only in the past few years has there been renewed interest in the fundamental principles underlying this paradigm. In this paper, we present a…

Artificial Intelligence · Computer Science 2009-09-25 S. Minton , J. Bresina , M. Drummond

How should social scientists understand and communicate the uncertainty of statistically estimated causal effects? I propose we utilize the posterior distribution of a causal effect and present the probability of the effect being greater…

Applications · Statistics 2022-11-15 Akisato Suzuki

We consider the problem of estimating the number of distinct elements in a large data set (or, equivalently, the support size of the distribution induced by the data set) from a random sample of its elements. The problem occurs in many…

Machine Learning · Computer Science 2021-06-17 Talya Eden , Piotr Indyk , Shyam Narayanan , Ronitt Rubinfeld , Sandeep Silwal , Tal Wagner

Laplace approximations are commonly used to approximate high-dimensional integrals in statistical applications, but the quality of such approximations as the dimension of the integral grows is not well understood. In this paper, we prove a…

Statistics Theory · Mathematics 2018-08-21 Helen Ogden

Time series classification is a task that aims at classifying chronological data. It is used in a diverse range of domains such as meteorology, medicine and physics. In the last decade, many algorithms have been built to perform this task…

Machine Learning · Computer Science 2021-06-16 Michael Franklin Mbouopda , Engelbert Mephu Nguifo

We address the common problem of calculating intervals in the presence of systematic uncertainties. We aim to investigate several approaches, but here describe just a Bayesian technique for setting upper limits. The particular example we…

Data Analysis, Statistics and Probability · Physics 2007-05-23 Joel Heinrich , Craig Blocker , John Conway , Luc Demortier , Louis Lyons , Giovanni Punzi , Pekka K. Sinervo

Recently Balakrishnan and Iliopoulos [Ann. Inst. Statist. Math. 61 (2009)] gave sufficient conditions under which maximum likelihood estimator (MLE) is stochastically increasing. In this paper we study test plans which are not considered…

Statistics Theory · Mathematics 2011-10-27 Piotr Nowak

Recently, several researchers have claimed that conclusions obtained from a Bayes factor (or the posterior odds) may contradict those obtained from Bayesian posterior estimation. In this short paper, we wish to point out that no such…

Statistics Theory · Mathematics 2022-10-24 Harlan Campbell , Paul Gustafson

When a series of measurements is performed with increasingly coarse (or increasingly fine) precision, consecutive observations seem to be erratically distributed at first, and then organize themselves into cycles and patterns. The patterns,…

chao-dyn · Physics 2009-10-22 George G. Szpiro

We set up a model for reasoning about metric spaces with belief theoretic measures. The uncertainty in these spaces stems from both probability and metric. To represent both aspect of uncertainty, we choose an expected distance function as…

Artificial Intelligence · Computer Science 2012-07-02 Seunghwan Lee