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In this article we present the Durrmeyer variant of generalized Bernstein operators that preserve the constant functions involving non-negative parameter ?. We derive the approximation behaviour of these operators including global…

Classical Analysis and ODEs · Mathematics 2018-08-07 Arun Kajla , Meenu Goyal

In the current article, we establish a distinct version of the operators defined by Berwal \emph{et al.}, which is the Kantorovich type modification of $\alpha$-Bernstein operators to approximate Lebesgue's integrable functions. We define…

Classical Analysis and ODEs · Mathematics 2024-09-27 Jaspreet Kaur , Meenu Goyal , Khursheed J. Ansari

The present paper deals with the Stancu type generalization of the Kantorovich discrete $q$-Beta operators. We establish some direct results, which include the asymptotic formula and error estimation in terms of the modulus of continuity…

Classical Analysis and ODEs · Mathematics 2015-08-25 Preeti Sharma , Vishnu Narayan Mishra

In this paper, we consider objective Bayesian inference of the generalized exponential distribution using the independence Jeffreys prior and validate the propriety of the posterior distribution under a family of structured priors. We…

Methodology · Statistics 2023-09-26 Aojun Li , Keying Ye , Min Wang

In this paper, using the concept of $A$-statistical convergence which is a regular (non-matrix) summability method, we obtain a general Korovkin type approximation theorem which concerns the problem of approximating a function $f$ by means…

Classical Analysis and ODEs · Mathematics 2007-05-23 Esra Erkus , Oktay Duman

The article deals with the generalized Newton--Kantorovich method for solving operator equations with nondifferentiable operators in Banach spaces. The convergence theorem is proved by means of majorant scalar equations.

Numerical Analysis · Mathematics 2012-01-12 A. N. Tanyhina

This article discusses the convergence properties of the Max Product and Max Min variants of Durrmeyer type exponential sampling series. We first establish pointwise and uniform convergence of both operators in the space of log uniformly…

Functional Analysis · Mathematics 2025-10-17 Satyaranjan Pradhan , Abhishek Senapati , Madan Mohan Soren

In this paper, we investigate the approximation properties of the summation-integral type operators as defined by Mishra et al. (Boll. Unione Mat. Ital. (2016) 8:297-305) and determine the local results as well as prove the convergence…

Functional Analysis · Mathematics 2020-02-25 Rishikesh Yadav , Ramakanta Meher , Vishnu Narayan Mishra

In this paper, we develop category theory of Markov kernels to study categorical aspects of Bayesian inversions. As a result, we present a unified model for Bayesian supervised learning, encompassing Bayesian density estimation. We…

Statistics Theory · Mathematics 2025-07-08 Hông Vân Lê

In this paper, we introduce a new sequence of operators based on the Gr\"unwald interpolation operators on Chebyshev nodes on the space $L^p[0,{\pi}]$. The operators we consider are integral variants of the Gr\"unwald interpolation…

Functional Analysis · Mathematics 2026-04-21 P. C. Vinaya

In this paper, we develop a general approach to proving global and local uniform limit theorems for the Horvitz-Thompson empirical process arising from complex sampling designs. Global theorems such as Glivenko-Cantelli and Donsker…

Statistics Theory · Mathematics 2019-05-31 Qiyang Han , Jon A. Wellner

his paper deals with approximating properties of the newly defined $q$-generalization of the Sz\'{a}sz operators in the case $q>1$. Quantitative estimates of the convergence in the polynomial weighted spaces and the Voronovskaja's theorem…

Functional Analysis · Mathematics 2010-05-24 Nazim I. Mahmudov

We introduce and study the class of linear transfers between probability distributions and the dual class of Kantorovich operators between function spaces. Linear transfers can be seen as an extension of convex lower semi-continuous…

Analysis of PDEs · Mathematics 2019-06-25 Malcolm Bowles , Nassif Ghoussoub

Vapnik--Chervonenkis' theorem is a seminal result in machine learning. It establishes sufficient conditions for empirical probabilities to converge to theoretical probabilities, uniformly over families of events. It also provides an…

Machine Learning · Computer Science 2026-01-26 A. Iosevich , A. Vagharshakyan , E. Wyman

This paper offers a newly created integral approach for operators employing the orthogonal modified Laguerre polynomials and P\u{a}lt\u{a}nea basis. These operators approximate the functions over the interval $[0,\infty)$. Further, the…

Functional Analysis · Mathematics 2024-05-14 Kapil Kumar , Naokant Deo , Durvesh Kumar Verma

We give a number of results on approximations of Markov kernels in total variation and Wasserstein norms weighted by a Lyapunov function. The results are applied to examples from Bayesian statistics where approximations to transition…

Probability · Mathematics 2018-07-09 James E. Johndrow , Jonathan C. Mattingly

There are many results on the simultaneous approximation by sequences of special positive linear operators. In the year 1978, Ismail and May as well as Volkov independently studied operators of exponential type covering the most classical…

Classical Analysis and ODEs · Mathematics 2023-09-19 Ulrich Abel

We systematically study the moduli theory of symplectic varieties (in the sense of Beauville) which admit a resolution by an irreducible symplectic manifold. In particular, we prove an analog of Verbitsky's global Torelli theorem for the…

Algebraic Geometry · Mathematics 2021-01-07 Benjamin Bakker , Christian Lehn

The aim of this paper is to introduce a generalization of the (p,q)-Bleimann-Butzer-Hahn operators based on (p,q)-integers and obtain Korovkin's type statistical approximation theorem for these operators. Also, we establish the rate of…

Classical Analysis and ODEs · Mathematics 2015-11-27 M. Mursaleen , Taqseer Khan

Bayesian inference for exponential family random graph models (ERGMs) is a doubly-intractable problem because of the intractability of both the likelihood and posterior normalizing factor. Auxiliary variable based Markov Chain Monte Carlo…

Computation · Statistics 2020-07-15 Fan Yin , Carter T. Butts
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