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Statistical mechanics for states with complex eigenvalues, which are described by Gel'fand triplet and represent unstable states like resonances, are discussed on the basis of principle of equal ${\it a priori}$ probability. A new entropy…

Statistical Mechanics · Physics 2016-08-31 T. Kobayashi , T. Shimbori

Based on a rough path foundation, we develop a model-free approach to stochastic portfolio theory (SPT). Our approach allows to handle significantly more general portfolios compared to previous model-free approaches based on F{\"o}llmer…

Probability · Mathematics 2023-06-19 Andrew L. Allan , Christa Cuchiero , Chong Liu , David J. Prömel

We study the effect of parameter uncertainty on a stochastic diffusion model, in particular the impact on the pricing of contingent claims, using methods from the theory of Dirichlet forms. We apply these techniques to hedging procedures in…

Pricing of Securities · Quantitative Finance 2012-03-27 Simone Scotti

We develop a framework for stochastic portfolio theory (SPT), which incorporates modern nonlinear price impact and impact decay models. Our main result is the derivation of the celebrated master formula for additive functional generation of…

Mathematical Finance · Quantitative Finance 2026-04-15 David Itkin

We examine the spectral structure of the two-dimensional advection-diffusion operator in flows with mixed phase space at very large Peclet number. Using Fourier discretization combined with symmetry reduction and Krylov-Arnoldi methods, we…

Chaotic Dynamics · Physics 2026-02-05 Christopher Amey , Bala Sundaram , Andrew C. Poje

We study the mean field limit of a rank-based model with common noise, which arises as an extension to models for the market capitalization of firms in stochastic portfolio theory. We show that, under certain conditions on the drift and…

Probability · Mathematics 2024-06-12 Mykhaylo Shkolnikov , Lane Chun Yeung

In this paper we introduce a simple continuous-time asset pricing framework, based on general multi-dimensional diffusion processes, that combines semi-analytic pricing with a nonlinear specification for the market price of risk. Our…

Statistical Finance · Quantitative Finance 2009-11-06 Aleksandar Mijatovic , Paul Schneider

We introduce a class of continuous planar processes, called "semimartingales on rays", and develop for them a change-of-variable formula involving quite general classes of test functions. Special cases of such planar processes are…

Probability · Mathematics 2017-04-18 Ioannis Karatzas , Minghan Yan

In this paper, we investigate a financial market model consisting of a risky asset, modeled as a general diffusion parameterized by a scale function and a speed measure, and a bank account process with a constant interest rate. This…

Mathematical Finance · Quantitative Finance 2025-12-09 Alexis Anagnostakis , David Criens , Mikhail Urusov

In this paper we investigate some properties, including causality, of a particular class of relativistic dissipative fluid theories of divergence type. This set is defined as those theories coming from a statistical description of matter,…

General Relativity and Quantum Cosmology · Physics 2008-11-26 Oscar A. Reula , Gabriel B. Nagy

We study a stochastic model for the diffusion of competing opinions in a population composed of three types of agents: trend-followers, opposers, and indifferent individuals. The decision dynamics are driven by reinforcement mechanisms,…

Probability · Mathematics 2025-06-24 Manuel González-Navarrete

In the present paper new light is shed on the non-central extensions of the Dirichlet distribution. Due to several probabilistic and inferential properties and to the easiness of parameter interpretation, the Dirichlet distribution proves…

Statistics Theory · Mathematics 2021-08-02 Carlo Orsi

Diffusion Models (DMs), as a leading class of generative models, offer key advantages for reinforcement learning (RL), including multi-modal expressiveness, stable training, and trajectory-level planning. This survey delivers a…

Machine Learning · Computer Science 2025-10-15 Changfu Xu , Jianxiong Guo , Yuzhu Liang , Haiyang Huang , Haodong Zou , Xi Zheng , Shui Yu , Xiaowen Chu , Jiannong Cao , Tian Wang

We introduce a class of group endomorphisms -- those of finite combinatorial rank -- exhibiting slow orbit growth. An associated Dirichlet series is used to obtain an exact orbit counting formula, and in the connected case this series is…

Dynamical Systems · Mathematics 2013-05-28 G. Everest , R. Miles , S. Stevens , T. Ward

Predictable forward performance processes (PFPPs) are stochastic optimal control frameworks for an agent who controls a randomly evolving system but can only prescribe the system dynamics for a short period ahead. This is a common scenario…

Mathematical Finance · Quantitative Finance 2024-03-26 Bahman Angoshtari , Shida Duan

We define triangulated factorization systems on triangulated categories, and prove that a suitable subclass thereof (the normal triangulated torsion theories) corresponds bijectively to $t$-structures on the same category. This result is…

Category Theory · Mathematics 2018-02-13 Fosco Loregian , Simone Virili

Motivated by the study of resolvent estimates in the presence of trapping, we prove a semiclassical propagation theorem in a neighborhood of a compact invariant subset of the bicharacteristic flow which is isolated in a suitable sense.…

Analysis of PDEs · Mathematics 2013-08-28 Kiril Datchev , András Vasy

Different theoretical methods used for the description of diffractive processes in small-x deep inelastic scattering are reviewed. The semiclassical approach, where a partonic fluctuation of the incoming virtual photon scatters off a…

High Energy Physics - Phenomenology · Physics 2009-02-20 A. Hebecker

Traditionally, the Dirichlet-multinomial distribution has been recognized as a key model for contingency tables generated by cluster sampling schemes. There are, however, other possible distributions appropriate for these contingency…

Methodology · Statistics 2016-09-26 Juana M. Alonso-Revenga , Nirian Martin , Leandro Pardo

Probabilistic generative models provide a flexible and systematic framework for learning the underlying geometry of data. However, model selection in this setting is challenging, particularly when selecting for ill-defined qualities such as…

Machine Learning · Computer Science 2022-10-05 Chester Holtz , Gal Mishne , Alexander Cloninger