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When using complex Bayesian models to combine information, the checking for consistency of the information being combined is good statistical practice. Here a new method is developed for detecting prior-data conflicts in Bayesian models…

Methodology · Statistics 2016-11-29 David J. Nott , Xueou Wang , Michael Evans , Berthold-Georg Englert

Dirichlet distribution and Dirichlet process as its infinite dimensional generalization are primarily used conjugate prior of categorical and multinomial distributions in Bayesian statistics. Extensions have been proposed to broaden…

Methodology · Statistics 2014-12-05 Xuenan Feng

In this present work, we discuss the Bayesian inference for the bivariate pseudo-exponential distribution. Initially, we assume independent gamma priors and then pseudo-gamma priors for the pseudo-exponential parameters. We are primarily…

Methodology · Statistics 2023-06-27 Banoth Veeranna

The axiomatic foundation of probability theory presented by Kolmogorov has been the basis of modern theory for probability and statistics. In certain applications it is, however, necessary or convenient to allow improper (unbounded)…

Statistics Theory · Mathematics 2017-11-07 Bo H. Lindqvist , Gunnar Taraldsen

We discuss Bayesian inference for parameters selected using the data. First, we provide a critical analysis of the existing positions in the literature regarding the correct Bayesian approach under selection. Second, we propose two types of…

Statistics Theory · Mathematics 2021-05-12 Daniel G. Rasines , G. Alastair Young

Benford's law predicts the occurrence of the $n^{\mathrm{th}}$ digit of numbers in datasets originating from various sources of the world, ranging from financial data to atomic spectra. It is intriguing that although many features of…

Popular Physics · Physics 2014-09-11 T. Alexopoulos , S. Leontsinis

In the present work we prove a number of surprising results about gaps between consecutive primes and arithmetic progressions in the sequence of generalized twin primes which could not have been proven without the recent fantastic…

Number Theory · Mathematics 2013-05-28 Janos Pintz

We provide a new characterization of the Dirichlet distribution. This characterization implies that under assumptions made by several previous authors for learning belief networks, a Dirichlet prior on the parameters is inevitable.

Artificial Intelligence · Computer Science 2013-02-21 Dan Geiger , David Heckerman

We consider the Jeffreys-Lindley paradox from an objective Bayesian perspective by attempting to find priors representing complete indifference to sample size in the problem. This means that we ensure that the prior for the unknown mean and…

Methodology · Statistics 2022-04-15 Andrew Fowlie

The Twin Prime conjecture states that there are infinitely many pairs of distinct primes which differ by $2$. Until recently this conjecture had seemed to be far out of reach with current techniques. However, in April 2013, Yitang Zhang…

Number Theory · Mathematics 2014-10-31 Andrew Granville

We study the distribution of primes from a topological viewpoint. Certain conjecture is introduced, and we show that it is equivalent to the Riemann Hypothesis.

Number Theory · Mathematics 2017-11-09 Kazunori Noguchi

The Lutz-Kelker correction is intended to give an unbiased estimate for stellar parallaxes and magnitudes, but it is shown explicitly that it does not. This paradox results from the application of an argument about sample statistics to the…

Astrophysics of Galaxies · Physics 2015-06-22 Charles Francis

We consider the fractional posterior distribution that is obtained by updating a prior distribution via Bayes theorem with a fractional likelihood function, a usual likelihood function raised to a fractional power. First, we analyze the…

Statistics Theory · Mathematics 2016-11-08 Anirban Bhattacharya , Debdeep Pati , Yun Yang

In statistical inference, uncertainty is unknown and all models are wrong. That is to say, a person who makes a statistical model and a prior distribution is simultaneously aware that both are fictional candidates. To study such cases,…

Machine Learning · Computer Science 2023-02-13 Sumio Watanabe

While the choice of prior is one of the most critical parts of the Bayesian inference workflow, recent Bayesian deep learning models have often fallen back on vague priors, such as standard Gaussians. In this review, we highlight the…

Machine Learning · Statistics 2022-03-21 Vincent Fortuin

A central challenge in statistical inference is the presence of confounding variables that may distort observed associations between treatment and outcome. Conventional "causal" methods, grounded in assumptions such as ignorability, exclude…

Methodology · Statistics 2025-09-09 Ellis Scharfenaker , Duncan K. Foley

In this paper, we consider pairs of a prime and a prime power with a fixed difference. We prove an average result on the distribution of such pairs. This is a partial improvement of the result of Bauer (1998).

Number Theory · Mathematics 2016-10-31 Yuta Suzuki

The Bateman-Horn conjecture is a far-reaching statement about the distribution of the prime numbers. It implies many known results, such as the prime number theorem and the Green-Tao theorem, along with many famous conjectures, such the…

Number Theory · Mathematics 2021-02-05 Soren Laing Aletheia-Zomlefer , Lenny Fukshansky , Stephan Ramon Garcia

Using instruments comprising ordered responses to items are ubiquitous for studying many constructs of interest. However, using such an item response format may lead to items with response categories infrequently endorsed or unendorsed…

Methodology · Statistics 2024-05-02 R. Noah Padgett , Grant B. Morgan , Tim Lomas

In this paper, we propose a novel Bayesian approach for nonparametric estimation in Wicksell's problem. This has important applications in astronomy for estimating the distribution of the positions of the stars in a galaxy given projected…

Statistics Theory · Mathematics 2025-09-01 Francesco Gili , Geurt Jongbloed , Aad van der Vaart