Skip to content
Free planNo credit card requiredStart writing
AI for Research home
AI for Research home
Dashboard
Sign inGet started

Search 250 million research papers

Read the abstract, see who wrote it and who cites it, and get the citation in APA, MLA, Chicago or Harvard.

About 10,000 papers for “Marginal distribution”

Subcinema: theorizing marginal film distribution

Ramón Lobato · Swinburne Research Bank (Swinburne University of Technology) · 2024

Between the multiplex and the art house lays a terrain of global film culture, which is inadequately accounted for in much contemporary film theory. Piracy operations, diasporic networks, cult markets, popular video industries--circuits such as these operate outside and alongside conventional channels of film distribution. Profit-oriented, non-resistant and largely informal, they deliver vast amounts of media to audiences every day. But how are we to study them? This article seeks to trace the outlines of a theoretical approach to film distribution, a field of study which Sean Cubitt has recently described as being only 'in its infancy'. A model of 'subcinema' is proposed here as a new way of conceptualising those forms of subterranean audiovisual exchange which do not show up on other maps.

Cited by 36Open access

The Marginal Productivity Theory of Distribution

Jack Harvey · 1985

How the product is distributed depends upon the rewards the various factors of production receive. We study: (a) the price of the service rendered by a factor; (b) the price of factors of production in a particular, occupation or district.

Cited by 2

A Bivariate Marginal Distribution Genetic Model

Marco Carpentieri · 2006

We introduce a genetic model based on simulated crossover of fixed sequences of two bit genes. States and dynamics of the deterministic genetic system, represented by the model , are derived in the case of infinite populations and for finite fitness functions (expressed in terms of multivariate polynomials). The genetic algorithm (simulating the proposed infinite population system) is experimentally compared with the infinite population genetic algorithm with bit-based simulated crossover for the MAC-CUT problem.

Cited by 7

The Marginal Productivity Theory of Distribution

John Pullen · 2009

The Marginal Productivity Theory of Distribution (MPTD) claims that in a free-market economy the demand for a factor of production will depend upon its marginal product – where "marginal product" is defined as the change in total product that is caused by, or that follows, the addition or subtraction of the marginal unit of the factor used in the production process, with all other inputs held constant. From its inception in the early nineteenth century the MPTD has been claimed by some economists to be a solution to the ethical problem of distributive justice, i.e. to be a means of determining fairness in wages, profits, interest and rent. Other economists have rejected this ethical claim, but have seen the MPTD as a valid demand-side criterion in the determination of equilibrium and efficiency. This book argues that the MPTD is valid, neither as a normative theory of social justice, nor as a positive law of economics. It suggests that economics is yet to develop a satisfactory theory of distribution that is scientific in the quantitative or mathematical sense. Through a survey of the origin and subsequent evolution of the MPTD in the writings of over 50 contributors over 150 years, John Pullen presents a critical history of the concept. The book begins by examining the conceptual tools that have been deployed to facilitate this analysis of past contributions to the MPTD and then looks at various economists and their contribution to the debate including its supporters such as Wicksteed, Marshall, Wicksell and Stigler, and its critics such as Pareto, Hobson, Edgeworth, Adriance and Cassel.

Cited by 17

Pair plot of marginal posterior distribution.

Laura Marie Helleckes (12206910), Michael Osthege (12206913), Wolfgang Wiechert (39471) et al. · Figshare · 2022

<p>Axis labels mark the 90% HDI and subplot axis limits are set at the 98% HDI. Units are <i>h</i><sup>−1</sup> for <i>μ</i><sub>max</sub>, for <i>Y</i><sub>XS</sub> and for <i>S</i><sub>0</sub> and <i>X</i><sub>0</sub>.</p> <p>(PDF)</p>

Cited by 0Open access

Marginal Densities of the Wishart Distribution

Veleva, Evelina · Bulgarian Digital Mathematics Library (BulDML) at IMI-BAS (Institute of Mathematics and Informatics) · 2016

We consider marginal densities obtained by elimination of non-diagonal elements of a positive definite random matrix with an arbitrary distribution.For a p p random matrix W such a marginal density is presented by a graph with p vertices.For every non-diagonal element of W, included in the density we draw in the graph an undirected edge between the corresponding vertices.By giving an equivalent definition of decomposable graphs we show that the bounds of the integration with respect to every excluded element of W can be exactly obtained if and only if the corresponding graph is decomposable.The author gives in an explicit form some of the marginal densities of an arbitrary Wishart distribution.

Cited by 0Open access

Marginal hazard distribution in network structures

Juozas Augutis, Ričardas Krikštolaitis, Elvyra Augutienė · Lietuvos matematikos rinkinys · 2023

The subject of this work is the analysis of hazard distribution in the network systems. In the age when transport flows in the railway and road systems, information flows in the internet channels, etc. intensify, hazard distribution in the network systems becomes a more relevant and important issue. In the paper various theoretical hazard type distributions in the network systems are analysed. Mathematical model of hazard distribution and accumulation in the nodes of the network was created.

Cited by 0Open access

Estimations of the marginal distribution models.

Xin Hu (38924), Bo Zhu (118038), Bokai Zhang (5856737) et al. · Figshare · 2024

<div><p>The linkages between the US and China, the world’s two major agricultural powers, have brought great uncertainty to the global food markets. Inspired by these, this paper examines the extreme risk spillovers between US and Chinese agricultural futures markets during significant crises. We use a copula-conditional value at risk (CoVaR) model with Markov-switching regimes to capture the tail dependence in their pair markets. The study covers the period from January 2006 to December 2022 and identifies two distinct dependence regimes (stable and crisis periods). Moreover, we find significant and asymmetric upside/downside extreme risk spillovers between the US and Chinese markets, which are highly volatile in crises. Additionally, the impact of international capital flows (the financial channel) on risk spillovers is particularly pronounced during the global financial crisis. During the period of the COVID-19 pandemic and the Russia-Ukraine 2022 war, the impact of supply chain disruptions (the non-financial channel) is highlighted. Our findings provide a theoretical reference for monitoring the co-movements in agricultural futures markets and practical insights for managing investment portfolios and enhancing food market stability during crises.</p></div>

Cited by 0Open access

Image Registration Via Marginal Distribution Adaptation

Xiaohu Yan, Jinfeng Yang, Xiaohua Liu et al. · IEEE Geoscience and Remote Sensing Letters · 2021

The distribution of feature vectors plays a critical role in image registration. In this letter, we propose a novel approach for remote sensing image registration based on marginal distribution adaptation. First, we map the feature vectors of reference and sensed images into a latent space. Transfer component analysis (TCA) is employed to compute the transformation matrix by minimizing the maximum mean discrepancy (MMD). Then, we match feature vectors in the latent space where their marginal distributions are similar, which can increase correct correspondences and enhance registration accuracy. Finally, we test the proposed algorithm on ten real image pairs. The effectiveness and efficiency of our approach are verified by experimental results.

Cited by 0

Average Marginal Loss Allocation of Distribution Networks

Kushal Manoharrao Jagtap · 2021

This paper proposes a new method for allocation of losses to consumers in distribution systems (DSs) under the deregulated environment of power systems. Proposed method follows the principle of electrical circuit to determine the power flow in the distribution branches and adopts branch oriented approach. It identifies the marginal contribution of real and imaginary part of load current in system losses corresponding to their power consumptions at nodes. Apart from traditional marginal loss coefficient (MLC) methods, however, proposed method recovers entire system losses without an additional step of reconciliation. Average marginal contribution of both real and imaginary part of load current makes proposed method more efficient and adoptable specially for radial structure of DSs. In order to test the proposed method, 28-bus DS is considered. Results of the proposed method are compared and validated with than that of other available methods in literature.

Cited by 3

Do PAC-Learners Learn the Marginal Distribution?

Max Hopkins, Daniel M. Kane, Shachar Lovett et al. · arXiv (Cornell University) · 2023

The Fundamental Theorem of PAC Learning asserts that learnability of a concept class $H$ is equivalent to the $\textit{uniform convergence}$ of empirical error in $H$ to its mean, or equivalently, to the problem of $\textit{density estimation}$, learnability of the underlying marginal distribution with respect to events in $H$. This seminal equivalence relies strongly on PAC learning's `distribution-free' assumption, that the adversary may choose any marginal distribution over data. Unfortunately, the distribution-free model is known to be overly adversarial in practice, failing to predict the success of modern machine learning algorithms, but without the Fundamental Theorem our theoretical understanding of learning under distributional constraints remains highly limited. In this work, we revisit the connection between PAC learning, uniform convergence, and density estimation beyond the distribution-free setting when the adversary is restricted to choosing a marginal distribution from a known family $\mathscr{P}$. We prove that while the traditional Fundamental Theorem indeed fails, a finer-grained connection between the three fundamental notions continues to hold: 1. PAC-Learning is strictly sandwiched between two refined models of density estimation, differing only in whether the learner $\textit{knows}$ the set of well-estimated events in $H$. 2. Under reasonable assumptions on $H$ and $\mathscr{P}$, density estimation is equivalent to $\textit{uniform estimation}$, a relaxation of uniform convergence allowing non-empirical estimators. Together, our results give a clearer picture of how the Fundamental Theorem extends beyond the distribution-free setting and shed new light on the classically challenging problem of learning under arbitrary distributional assumptions.

Cited by 0Open access

Some Marginal Densities of the Wishart Distribution

Veleva, Evelina · Bulgarian Digital Mathematics Library (BulDML) at IMI-BAS (Institute of Mathematics and Informatics) · 2012

Евелина Илиева Велева - Разпределението на Уишарт се среща в практиката като разпределението на извадъчната ковариационна матрица за наблюдения над многомерно нормално разпределение. Изведени са някои маргинални плътности, получени чрез интегриране на плътността на Уишарт разпределението. Доказани са необходими и достатъчни условия за положителна определеност на една матрица, които дават нужните граници за интегрирането.

Cited by 0Open access

ESTIMATION OF JOINT DISTRIBUTION FROM MARGINAL DISTRIBUTIONS

Yogendra P. Chaubey, Fassil Nebebe, Krzystof Dzicielowski · 2003

In many consumer surveys, demographic and other data are used as covariates for predictions of con-sumer preferences and other decision variables. How-ever, to protect the confidentiality of the consumers, the data are only available in marginal frequency dis-tribution format. This creates a problem in predict-ing joint frequencies required for further decision-making. In this paper we consider the dependence structure in formulating the joint distribution given in the form of a Dirichlet prior. The computations in the case of 2 × 2 table are extended to multiway con-tingency tables and shown to provide similar results as obtained using the Monte Carlo methods. 1

Cited by 1