惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

爱范儿
爱范儿
Y
Y Combinator Blog
博客园 - Franky
D
Docker
B
Blog RSS Feed
M
MIT News - Artificial intelligence
雷峰网
雷峰网
博客园 - 司徒正美
人人都是产品经理
人人都是产品经理
宝玉的分享
宝玉的分享
S
SegmentFault 最新的问题
GbyAI
GbyAI
Recent Announcements
Recent Announcements
Martin Fowler
Martin Fowler
H
Hackread – Cybersecurity News, Data Breaches, AI and More
MyScale Blog
MyScale Blog
B
Blog
H
Help Net Security
Microsoft Security Blog
Microsoft Security Blog
WordPress大学
WordPress大学
Vercel News
Vercel News
The Cloudflare Blog
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
Google DeepMind News
Google DeepMind News

math.PR updates on arXiv.org

Visibility in the Boolean Model on Harmonic Manifolds Global estimates on the Brenier map Geodesics and Wandering Exponents in Brochette First-Passage Percolation State-dependent inverse-subordinator time changes of regenerative processes: Excursion structure and multiscale occupation-time limits Randomly twisted transfer operators and singular values statistics Generalized Bessel-Dunkl diffusions An almost sure invariance principle for the Takagi-van der Waerden class functions Central limit theorems for high dimensional lattice polytopes: cosmological polytopes Convergence rate estimates for semigroups and heat kernels associated with resistance forms Second-order Poincaré inequalities and localization on the Poisson space Maximum Probability of Independence in Transitive Matroids On global solutions to the semidiscrete stochastic heat equation The Poisson Tail Conjecture for primes in short intervals A Complete Spectral Analysis of the CEV Operator with Applications to Arbitrage Holographic functions and neural networks From Betting to Empirical Bernstein LIL Concentration of General Stochastic Approximation Under Heavy-Tailed Markovian Noise Pointwise Generalization in Deep Neural Networks Bayesian Latent Space Models for Graphs Are Misspecified: Toward Robust Inference via Generalized Posteriors Wasserstein bounds for denoising diffusion probabilistic models via the Föllmer process A note on connections between the Föllmer process and the denoising diffusion probabilistic model Simple Approximation and Derivative Free Inference-Time Scaling for Diffusion Models via Sequential Monte Carlo on Path Measures Diffusion-Based Stochastic Operator Networks for Uncertainty Quantification in Stochastic Partial Differential Equations A Fourier perspective on the learning dynamics of neural networks: from sample complexities to mechanistic insights Propagation of Chaos in Contextual Flow Maps Dimension-Uniform Discretization Analysis of Preconditioned Annealed Langevin Dynamics for Multimodal Gaussian Mixtures $α$-TCAV: A Unified Framework for Testing with Concept Activation Vectors Scaling Laws from Sequential Feature Recovery: A Solvable Hierarchical Model On the Limits of Latent Reuse in Diffusion Models State-of-art minibatches via novel DPP kernels: discretization, wavelets, and rough objectives
A probabilistic approach to Dirac concentration in nonloc...
2017-11-29 · via math.PR updates on arXiv.org

This work is devoted to the study of scaling limits in small mutations and large time of the solutions u^$ε$ of two deterministic models of phenotypic adaptation, where the parameter $ε$ > 0 scales the size of mutations. The first model is the so-called Lotka-Volterra parabolic PDE in R d with an arbitrary number of resources and the second one is an adaptation of the first model to a finite phenotype space. The solutions of such systems typically concentrate as Dirac masses in the limit $ε$ $\rightarrow$ 0. Our main results are, in both cases, the representation of the limits of $ε$ log u^$ε$ as solutions of variational problems and regularity results for these limits. The method mainly relies on Feynman-Kac type representations of u $ε$ and Varadhan's Lemma. Our probabilistic approach applies to multi-resources situations not covered by standard analytical methods and makes the link between variational limit problems and Hamilton-Jacobi equations with irregular Hamiltonians that arise naturally from analytical methods. The finite case presents substantial difficulties since the rate function of the associated large deviation principle has non-compact level sets. In that case, we are also able to obtain uniqueness of the solution of the variational problem and of the associated differential problem which can be interpreted as a Hamilton-Jacobi equation in finite state space.