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Related papers: T. E. Harris and branching processes

200 papers

Decision theories offer principled methods for making choices under various types of uncertainty. Algorithms that implement these theories have been successfully applied to a wide range of real-world problems, including materials and drug…

Machine Learning · Computer Science 2026-05-26 Agustinus Kristiadi

The general theory of the branching processes is used for establishing the relation between the parameters $k$ and $\bar n$ of the negative binomial distribution. This relation gives the possibility to describe the overall data on…

High Energy Physics - Phenomenology · Physics 2009-10-22 S. G. Matinyan , E. B. Prokhorenko

The likelihood function is central to both frequentist and Bayesian formulations of parametric statistical inference, and large-sample approximations to the sampling distributions of estimators and test statistics, and to posterior…

Methodology · Statistics 2022-04-05 Anthony C. Davison , Nancy Reid

Published exactly seventy years ago, Jeffreys's Theory of Probability (1939) has had a unique impact on the Bayesian community and is now considered to be one of the main classics in Bayesian Statistics as well as the initiator of the…

Statistics Theory · Mathematics 2010-10-11 Christian P. Robert , Nicolas Chopin , Judith Rousseau

In this paper we consider a model based on branching process theory for the proliferation and the dissemination network of T cells in the adaptive immune response. A multi-type Galton Watson branching process is assumed as the basic…

Quantitative Methods · Quantitative Biology 2015-06-09 Alessandro Boianelli , Antonio Vicino

The mathematical achievements of Harry Kesten since the mid-1950s have revolutionized probability theory as a subject in its own right and in its associations with aspects of algebra, analysis, geometry, and statistical physics. Through his…

History and Overview · Mathematics 2020-03-23 Geoffrey R. Grimmett , Gregory F. Lawler

Inductive inference is a recursion-theoretic theory of learning, first developed by E. M. Gold (1967). This paper surveys developments in probabilistic inductive inference. We mainly focus on finite inference of recursive functions, since…

Machine Learning · Computer Science 2007-05-23 Andris Ambainis

Not only did Turing help found one of the most exciting areas of modern science (computer science), but it may be that his contribution to our understanding of our physical reality is greater than we had hitherto supposed. Here I explore…

Computational Complexity · Computer Science 2014-08-01 Hector Zenil

The book A Treatise on Probability was published by John Maynard Keynes in 1921. It contains a critical assessment of the foundations of probability and of the current statistical methodology. As a modern reader, we review here the aspects…

Statistics Theory · Mathematics 2010-10-19 Christian P. Robert

In the footsteps of the book \textit{Measure Theory and Integration By and For the Learner} of our series in Probability Theory and Statistics, we intended to devote a special volume of the very probabilistic aspects of the first cited…

Probability · Mathematics 2018-08-07 Gane Samb Lo

Multivariate Hawkes Processes (MHPs) are an important class of temporal point processes that have enabled key advances in understanding and predicting social information systems. However, due to their complex modeling of temporal…

Machine Learning · Computer Science 2020-03-02 Maximilian Nickel , Matthew Le

Branching processes are a class of continuous-time Markov chains (CTMCs) with ubiquitous applications. A general difficulty in statistical inference under partially observed CTMC models arises in computing transition probabilities when the…

Computation · Statistics 2015-03-10 Jason Xu , Vladimir N. Minin

This is a 1971 dissertation. Only its extended abstract was published at the time. While some results appeared in other publications, a number of details remained unpublished and may still have relevance.

Information Theory · Computer Science 2018-12-03 Leonid A. Levin

We give an account of matter and (basically) a solution of a new class of problems synthesizing percolation theory and branching diffusion processes. They led us to realizing a novel type of stochastic processes, namely branching processes…

Condensed Matter · Physics 2011-12-08 A. Mezhlumian , S. A. Molchanov

This text presents an unified approach of probability and statistics in the pursuit of understanding and computation of randomness in engineering or physical or social system with prediction with generalizability. Starting from elementary…

History and Overview · Mathematics 2024-01-19 Lakshman Mahto

This paper is a collection of recent results on discrete-time and continuous-time branching random walks. Some results are new and others are known. Many aspects of this theory are considered: local, global and strong local survival, the…

Probability · Mathematics 2018-05-07 Daniela Bertacchi , Fabio Zucca

The aim of this paper is to introduce a multitype branching process with random migration following the research initiated with the Galton-Watson process with migration introduced in [Yanev & Mitov (1980) C. R. Acad. Bulg. Sci.…

Probability · Mathematics 2024-09-10 Miguel González , Pedro Martín-Chávez , Inés del Puerto

We introduce a model-independent approximation for the branching ratio of Hawkes self-exciting point processes. Our estimator requires knowing only the mean and variance of the event count in a sufficiently large time window, statistics…

Statistical Finance · Quantitative Finance 2014-12-17 Stephen J. Hardiman , Jean-Philippe Bouchaud

We investigate a two-type critical Bellman--Harris branching process with the following properties: the tail of the life-length distribution of the first type particles is of order $o(t^{-2})$; the tail of the life-length distribution of…

Probability · Mathematics 2013-11-06 Vladimir Vatutin , Alexander Iksanov , Valentin Topchii

This article examines a recent body of work on stochastic processes indexed by a tree. Emphasis is on the application of this new framework to existing probability models. Proofs are largely omitted, with references provided.

Probability · Mathematics 2007-05-23 Robin Pemantle