Pinboard (cshalizi) https://pinboard.in/u:cshalizi/public/ recent bookmarks from cshalizi No Empirical Probability Measure can Converge in the Total Variation Sense for all Distributions 2025-03-10T13:31:23+00:00 https://projecteuclid.org/journals/annals-of-statistics/volume-18/issue-3/No-Empirical-Probability-Measure-can-Converge-in-the-Total-Variation/10.1214/aos/1176347765.full cshalizi to:NB probability re:almost_none via:? have_skimmed https://pinboard.in/ https://pinboard.in/u:cshalizi/b:f5168b5d65b6/ [2203.04395] Equivalences of Geometric Ergodicity of Markov Chains 2024-12-11T16:02:48+00:00 https://arxiv.org/abs/2203.04395 cshalizi to:NB markov_models mixing ergodic_theory re:almost_none https://pinboard.in/ https://pinboard.in/u:cshalizi/b:bb278df058e6/ [2403.16651] A short proof of the Dvoretzky--Kiefer--Wolfowitz--Massart inequality 2024-12-11T15:52:43+00:00 https://arxiv.org/abs/2403.16651 cshalizi to:NB empirical_processes to_read re:almost_none to_teach:childs_garden_of_statistical_learning_theory https://pinboard.in/ https://pinboard.in/u:cshalizi/b:847d245f036a/ Foundations of Modern Probability | SpringerLink 2024-10-10T13:45:49+00:00 https://link.springer.com/book/10.1007/978-3-030-61871-1 cshalizi to_read probability stochastic_processes re:almost_none downloaded books:owned books:recommended https://pinboard.in/ https://pinboard.in/u:cshalizi/b:44e800fda870/ An Elementary Theorem Concerning Stationary Ergodic Processes on JSTOR 2023-12-02T02:39:26+00:00 https://doi.org/10.1214/aoms/1177706638 cshalizi stochastic_processes ergodic_theory have_read breiman.leo re:almost_none in_NB https://pinboard.in/ https://pinboard.in/u:cshalizi/b:9b790bfccaaa/ Functional Itô Calculus by Bruno Dupire :: SSRN 2023-10-26T13:21:04+00:00 https://papers.ssrn.com/sol3/papers.cfm?abstract_id=1435551 cshalizi in_NB stochastic_differential_equations re:almost_none https://pinboard.in/ https://pinboard.in/u:cshalizi/b:48fc1211d3a6/ [2303.08992] Law of large numbers and central limit theorem for ergodic quantum processes 2023-04-22T13:55:53+00:00 https://arxiv.org/abs/2303.08992 cshalizi stochastic_processes quantum_mechanics ergodic_theory mixing central_limit_theorem re:almost_none in_NB https://pinboard.in/ https://pinboard.in/u:cshalizi/b:0ab8f9922f41/ Stochastic realization problems | SpringerLink 2023-03-27T15:07:23+00:00 https://link.springer.com/chapter/10.1007/bfb0008474 cshalizi to:NB stochastic_processes re:AoS_project re:almost_none via:mraginsky https://pinboard.in/ https://pinboard.in/u:cshalizi/b:f2eb81ce173e/ [2111.12603] Strong Invariance Principles for Ergodic Markov Processes 2022-06-19T17:05:18+00:00 https://arxiv.org/abs/2111.12603 cshalizi central_limit_theorem stochastic_processes convergence_of_stochastic_processes markov_models re:almost_none in_NB https://pinboard.in/ https://pinboard.in/u:cshalizi/b:23ae04823e1b/ Sampling Using Diffusion Processes, from Langevin to Schrödinger – The Information Structuralist 2022-06-05T15:48:25+00:00 https://infostructuralist.wordpress.com/2021/09/02/sampling-using-diffusion-processes-from-langevin-to-schrodinger/ cshalizistochastic_processes have_read simulation monte_carlo stochastic_differential_equations control_theory_and_control_engineering raginsky.maxim re:almost_none https://pinboard.in/ https://pinboard.in/u:cshalizi/b:697eaf9da922/ [2107.13837] A general Kolmogorov-Chentsov type theorem with applications to limit theorems for Banach-valued processes 2021-07-30T02:52:41+00:00 https://arxiv.org/abs/2107.13837 cshalizi to:NB stochastic_processes re:almost_none https://pinboard.in/ https://pinboard.in/u:cshalizi/b:1fb1ee744cd0/ [2107.08469] Quantitative Marcinkiewicz's theorem and central limit theorems: applications to spin systems and point processes 2021-07-27T12:05:52+00:00 https://arxiv.org/abs/2107.08469 cshalizi to:NB probability stochastic_processes central_limit_theorem point_processes statistical_mechanics re:almost_none https://pinboard.in/ https://pinboard.in/u:cshalizi/b:9bb1f648cb93/ [2011.00308] Mixing it up: A general framework for Markovian statistics 2021-06-25T14:55:04+00:00 https://arxiv.org/abs/2011.00308 cshalizi to:NB to_read markov_models minimax empirical_processes statistical_inference_for_stochastic_processes re:almost_none mixing statistics https://pinboard.in/ https://pinboard.in/u:cshalizi/b:60fea291f30b/ [2105.12717] A handy fluctuation-dissipation relation to approach generic noisy systems and chaotic dynamics 2021-06-01T17:29:27+00:00 https://arxiv.org/abs/2105.12717 cshalizi fluctuation-response stochastic_processes vulpiani.angelo re:almost_none in_NB https://pinboard.in/ https://pinboard.in/u:cshalizi/b:53f38218e357/ [2104.12024] A General Conditional Large Deviation Principle 2021-04-29T03:28:15+00:00 https://arxiv.org/abs/2104.12024 cshalizi large_deviations re:almost_none to_read in_NB https://pinboard.in/ https://pinboard.in/u:cshalizi/b:f6b2614ba676/ [2104.11505] Stochastic differential equations with irregular coefficients:~mind the gap! 2021-04-26T14:47:02+00:00 https://arxiv.org/abs/2104.11505 cshalizi to:NB stochastic_differential_equations re:almost_none https://pinboard.in/ https://pinboard.in/u:cshalizi/b:11ca9140c1d9/ [2104.08705] A theory of integration for Cesàro limits 2021-04-21T19:49:08+00:00 https://arxiv.org/abs/2104.08705 cshalizi to:NB analysis re:almost_none https://pinboard.in/ https://pinboard.in/u:cshalizi/b:d7658794eb00/ [2104.08365] Dobrushin and Steif metrics are equal 2021-04-21T14:38:42+00:00 https://arxiv.org/abs/2104.08365 cshalizi to:NB probability re:almost_none https://pinboard.in/ https://pinboard.in/u:cshalizi/b:a8c02f665018/ Path Integrals for Stochastic Processes 2021-04-16T18:10:53+00:00 https://www.worldscientific.com/worldscibooks/10.1142/8695 cshalizi stochastic_processes non-equilibrium feynman_diagrams_and_path_integrals re:almost_none in_NB have_read books:reviewed path_integrals_for_classical_stochastic_processes https://pinboard.in/ https://pinboard.in/u:cshalizi/b:066f0971f7a7/ Phys. Rev. E 103, 042126 (2021) - Temporal fluctuation scaling in nonstationary time series using the path integral formalism 2021-04-16T15:58:04+00:00 https://journals.aps.org/pre/abstract/10.1103/PhysRevE.103.042126 cshalizi stochastic_processes non-stationarity long-range_dependence re:almost_none feynman_diagrams_and_path_integrals in_NB path_integrals_for_classical_stochastic_processes https://pinboard.in/ https://pinboard.in/u:cshalizi/b:87934732b079/ [2101.02002] On the Feller-Dynkin and the Martingale Property of One-Dimensional Diffusions 2021-01-07T21:45:47+00:00 https://arxiv.org/abs/2101.02002 cshalizi to:NB stochastic_processes markov_models martingales re:almost_none https://pinboard.in/ https://pinboard.in/u:cshalizi/b:1d920b25cb18/ Thinking probabilistically: stochastic processes disordered systems and their applications | Mathematical modelling and methods | Cambridge University Press 2020-12-16T19:57:30+00:00 https://www.cambridge.org/9781108789981 cshalizi to:NB books:noted stochastic_processes re:almost_none books:suggest_to_library https://pinboard.in/ https://pinboard.in/u:cshalizi/b:9ae36a5056ec/ [2011.13780] Rate of convergence in Trotter's approximation theorem and its applications 2020-11-30T03:02:09+00:00 https://arxiv.org/abs/2011.13780 cshalizi to:NB markov_models convergence_of_stochastic_processes stochastic_processes re:almost_none approximation https://pinboard.in/ https://pinboard.in/u:cshalizi/b:df4597d34e61/ Stochastic stability of differential equations in abstract spaces | Differential and integral equations, dynamical systems and control | Cambridge University Press 2020-01-09T21:02:05+00:00 https://www.cambridge.org/us/academic/subjects/mathematics/differential-and-integral-equations-dynamical-systems-and-co/stochastic-stability-differential-equations-abstract-spaces?format=PB cshalizi in_NB stochastic_processes stochastic_differential_equations dynamical_systems hilbert_space books:noted re:almost_none books:suggest_to_library https://pinboard.in/ https://pinboard.in/u:cshalizi/b:0183c2ae8f33/ Stochastic dynamics filtering and optimization | Control systems and optimization | Cambridge University Press 2020-01-09T17:57:40+00:00 https://www.cambridge.org/us/academic/subjects/engineering/control-systems-and-optimization/stochastic-dynamics-filtering-and-optimization?format=HB cshalizi books:noted optimization filtering state_estimation stochastic_processes state-space_models re:almost_none books:suggest_to_library in_NB downloaded https://pinboard.in/ https://pinboard.in/u:cshalizi/b:30fbcfbf0054/ [1910.09319] Empirical Process of Multivariate Gaussian under General Dependence 2019-10-29T14:25:00+00:00 https://arxiv.org/abs/1910.09319 cshalizi gaussian_processes stochastic_processes empirical_processes re:almost_none in_NB https://pinboard.in/ https://pinboard.in/u:cshalizi/b:ac75e1d721b1/ Phys. Rev. E 100, 042135 (2019) - Anomalous scaling of dynamical large deviations of stationary Gaussian processes 2019-10-29T14:21:39+00:00 https://journals.aps.org/pre/abstract/10.1103/PhysRevE.100.042135 cshalizi2$ for the particular case of the Ornstein-Uhlenbeck process, holds for a whole class of stationary Gaussian processes."]]> to:NB large_deviations stochastic_processes re:almost_none gaussian_processes https://pinboard.in/ https://pinboard.in/u:cshalizi/b:330d38a198fd/ [1909.12990] Stochastic path integrals can be derived like quantum mechanical path integrals 2019-10-01T15:41:26+00:00 https://arxiv.org/abs/1909.12990 cshalizi stochastic_differential_equations stochastic_processes markov_models to_read re:almost_none feynman_diagrams_and_path_integrals in_NB path_integrals_for_classical_stochastic_processes https://pinboard.in/ https://pinboard.in/u:cshalizi/b:c5d08fd261b4/ [1909.07363] On an irreducibility type condition for the ergodicity of nonconservative semigroups 2019-09-17T14:00:59+00:00 https://arxiv.org/abs/1909.07363 cshalizi to:NB ergodic_theory markov_models stochastic_processes re:almost_none https://pinboard.in/ https://pinboard.in/u:cshalizi/b:3c54d60c247c/ [1404.2035] Strongly continuous and locally equi-continuous semigroups on locally convex spaces 2019-09-13T13:21:11+00:00 https://arxiv.org/abs/1404.2035 cshalizi to:NB algebra markov_models stochastic_processes re:almost_none https://pinboard.in/ https://pinboard.in/u:cshalizi/b:74efe87da53e/ [1412.8674] Infinite-dimensional stochastic differential equations and tail $ σ$-fields 2019-09-13T13:20:29+00:00 https://arxiv.org/abs/1412.8674 cshalizi to:NB stochastic_processes stochastic_differential_equations interacting_particle_systems re:almost_none https://pinboard.in/ https://pinboard.in/u:cshalizi/b:ead3733f354b/ [1909.05794] Stationary distributions of continuous-time Markov chains: a review of theory and truncation-based approximations 2019-09-13T13:19:39+00:00 https://arxiv.org/abs/1909.05794 cshalizi to:NB markov_models stochastic_processes re:almost_none approximation https://pinboard.in/ https://pinboard.in/u:cshalizi/b:78dca7d89c12/ [1909.05570] Sharp Large Deviations for empirical correlation coefficients 2019-09-13T13:05:23+00:00 https://arxiv.org/abs/1909.05570 cshalizi to:NB large_deviations statistics re:almost_none https://pinboard.in/ https://pinboard.in/u:cshalizi/b:8f417c7927c9/ [1909.00038] Limit theorems for generalized density-dependent Markov chains and bursty stochastic gene regulatory networks 2019-09-04T15:28:27+00:00 https://arxiv.org/abs/1909.00038 cshalizi to:NB biochemical_networks stochastic_processes markov_models re:almost_none https://pinboard.in/ https://pinboard.in/u:cshalizi/b:ff5c3428890d/ [1909.00042] Macroscopic limits, analytical distributions, and noise structure for stochastic gene expression with coupled feedback loops 2019-09-04T15:27:35+00:00 https://arxiv.org/abs/1909.00042 cshalizi to:NB biochemical_networks gene_expression_data_analysis markov_models stochastic_processes re:almost_none https://pinboard.in/ https://pinboard.in/u:cshalizi/b:2ca8c6250ee0/ [1810.04496] On maxima of stationary fields 2019-08-20T14:52:31+00:00 https://arxiv.org/abs/1810.04496 cshalizivn,MAn≤vn))+o(1), for some increasing sequence of sets An of size o(nd). For a class of fields satisfying a local mixing condition, including m-dependent ones, the theorem holds with a constant finite A replacing An. The above results lead to new formulas for the extremal index for random fields."]]> to:NB extreme_values random_fields stochastic_processes mixing re:almost_none https://pinboard.in/ https://pinboard.in/u:cshalizi/b:b48b11d4a911/ [1908.06703] Functional Limit Theorems for Marked Hawkes Point Measures 2019-08-20T14:52:03+00:00 https://arxiv.org/abs/1908.06703 cshalizi to:NB point_processes functional_central_limit_theorem stochastic_processes re:almost_none https://pinboard.in/ https://pinboard.in/u:cshalizi/b:d196fcbc8880/ [1810.07102] Lyapunov Criteria for the Feller-Dynkin Property of Martingale Problems 2019-08-19T13:22:29+00:00 https://arxiv.org/abs/1810.07102 cshalizi to:NB stochastic_processes martingales markov_models re:almost_none https://pinboard.in/ https://pinboard.in/u:cshalizi/b:11b131bac442/ [1806.09486] Building a path-integral calculus: a covariant discretization approach 2019-07-30T00:04:31+00:00 https://arxiv.org/abs/1806.09486 cshalizi stochastic_processes re:almost_none feynman_diagrams_and_path_integrals in_NB path_integrals_for_classical_stochastic_processes https://pinboard.in/ https://pinboard.in/u:cshalizi/b:4bd6d1cd17fd/ Salins : Equivalences and counterexamples between several definitions of the uniform large deviations principle 2019-06-06T13:40:41+00:00 https://projecteuclid.org/euclid.ps/1559289659 cshalizi to:NB large_deviations probability re:almost_none https://pinboard.in/ https://pinboard.in/u:cshalizi/b:5be3129ca4ba/ Highly Resolved Brownian Motion in Space and in Time | Annual Review of Fluid Mechanics 2019-05-26T18:05:19+00:00 https://www.annualreviews.org/doi/abs/10.1146/annurev-fluid-010518-040527 cshalizi to:NB brownian_motion statistical_mechanics physics re:almost_none https://pinboard.in/ https://pinboard.in/u:cshalizi/b:dab105efce21/ From Biology to Physics and Back: The Problem of Brownian Movement | Annual Review of Condensed Matter Physics 2019-05-26T17:55:16+00:00 https://www.annualreviews.org/doi/abs/10.1146/annurev-conmatphys-031218-013318 cshalizi to:NB brownian_motion thermodynamics re:almost_none https://pinboard.in/ https://pinboard.in/u:cshalizi/b:fdca1cd52951/ McGoff , Mukherjee , Pillai : Statistical inference for dynamical systems: A review 2019-05-25T02:31:11+00:00 https://projecteuclid.org/euclid.ssu/1447165229 cshalizi dynamical_systems statistical_inference_for_stochastic_processes stochastic_processes statistics re:almost_none re:stacs have_read in_NB https://pinboard.in/ https://pinboard.in/u:cshalizi/b:98dfc7d60875/ Functional Gaussian Approximation for Dependent Structures - Florence Merlevede; Magda Peligrad; Sergey Utev - Oxford University Press 2019-05-24T23:55:52+00:00 https://global.oup.com/academic/product/functional-gaussian-approximation-for-dependent-structures-9780198826941?cc=us&lang=en# cshalizi to:NB central_limit_theorem stochastic_processes convergence_of_stochastic_processes mixing ergodic_theory re:almost_none books:noted https://pinboard.in/ https://pinboard.in/u:cshalizi/b:a47e5db7c861/ Stochastic stability of differential equations in abstract spaces 2019-05-14T16:05:05+00:00 https://www.cambridge.org/9781108705172 cshalizi to:NB books:noted stochastic_differential_equations dynamical_systems hilbert_space stochastic_processes re:almost_none https://pinboard.in/ https://pinboard.in/u:cshalizi/b:4858396717c8/ [1903.01608] Theoretical guarantees for sampling and inference in generative models with latent diffusions 2019-05-13T13:37:45+00:00 https://arxiv.org/abs/1903.01608 cshalizi to:NB stochastic_processes inference_to_latent_objects statistical_inference_for_stochastic_processes neural_networks variational_inference raginsky.maxim have_skimmed re:almost_none control_theory_and_control_engineering https://pinboard.in/ https://pinboard.in/u:cshalizi/b:7cb225a6e2fa/ Diffusion by Continuous Movements - Taylor - 1922 - Proceedings of the London Mathematical Society - Wiley Online Library 2018-12-29T17:47:47+00:00 https://londmathsoc.onlinelibrary.wiley.com/doi/abs/10.1112/plms/s2-20.1.196 cshalizi stochastic_processes turbulence ergodic_theory probability have_skimmed taylor.g.i. physics re:almost_none to_teach:data_over_space_and_time in_NB have_read https://pinboard.in/ https://pinboard.in/u:cshalizi/b:93f1a3437d1f/ [1805.10721] Bernstein's inequality for general Markov chains 2018-12-07T16:46:45+00:00 https://arxiv.org/abs/1805.10721 cshalizi deviation_inequalities probability stochastic_processes markov_models re:almost_none in_NB https://pinboard.in/ https://pinboard.in/u:cshalizi/b:e2c2d3f7f9cb/ Phys. Rev. A 38, 2066 (1988) - Thermally induced escape: The principle of minimum available noise energy 2018-08-02T16:09:03+00:00 https://journals.aps.org/pra/abstract/10.1103/PhysRevA.38.2066 cshalizi have_read large_deviations stochastic_processes dynamical_systems non-equilibrium statistical_mechanics re:do-institutions-evolve re:almost_none in_NB https://pinboard.in/ https://pinboard.in/u:cshalizi/b:9cdf919b8a8b/ Activation energy for thermally induced escape from a basin of attraction - ScienceDirect 2018-08-02T16:05:06+00:00 https://www.sciencedirect.com/science/article/pii/0375960187901514 cshalizi have_read large_deviations non-equilibrium statistical_mechanics dynamical_systems stochastic_processes re:do-institutions-evolve re:almost_none in_NB https://pinboard.in/ https://pinboard.in/u:cshalizi/b:3730b1a68e91/ [1703.10007] A Course in Interacting Particle Systems 2018-07-02T20:59:46+00:00 https://arxiv.org/abs/1703.10007 cshalizi to:NB interacting_particle_systems stochastic_processes re:almost_none via:rvenkat https://pinboard.in/ https://pinboard.in/u:cshalizi/b:130c3ca6382d/ Kingman : Subadditive Ergodic Theory 2018-06-02T16:41:05+00:00 https://projecteuclid.org/euclid.aop/1176996798#abstract cshalizi in_NB stochastic_processes ergodic_theory have_read re:almost_none https://pinboard.in/ https://pinboard.in/u:cshalizi/b:9406a2c06f8b/ [1708.02890] Asymptotic equivalence of probability measures and stochastic processes 2018-01-31T02:46:26+00:00 https://arxiv.org/abs/1708.02890 cshalizi probability large_deviations stochastic_processes touchette.hugo re:almost_none via:rvenkat in_NB have_read https://pinboard.in/ https://pinboard.in/u:cshalizi/b:1f897c9fcf0f/ Large deviation principle for epidemic models | Journal of Applied Probability | Cambridge Core 2017-10-07T17:06:27+00:00 https://www.cambridge.org/core/journals/journal-of-applied-probability/article/large-deviation-principle-for-epidemic-models/4A3137B42A47143209DF65B22B37D331 cshalizi to:NB large_deviations epidemic_models stochastic_processes to_read re:almost_none re:do-institutions-evolve https://pinboard.in/ https://pinboard.in/u:cshalizi/b:6e3023ce51a3/ Foundations of Ergodic Theory | Abstract Analysis | Cambridge University Press 2016-09-30T17:34:34+00:00 http://cambridge.org/9781107126961 cshalizi ergodic_theory mathematics stochastic_processes probability books:noted re:almost_none in_NB https://pinboard.in/ https://pinboard.in/u:cshalizi/b:4fd06d026b96/ Barron : Entropy and the Central Limit Theorem 2016-04-15T21:44:51+00:00 https://projecteuclid.org/euclid.aop/1176992632 cshalizi to:NB to_read information_theory central_limit_theorem probability barron.andrew_w. re:almost_none via:tslumley https://pinboard.in/ https://pinboard.in/u:cshalizi/b:8c0f85a0ad8f/ Convergence of One-parameter Operator Semigroups | Abstract Analysis | Cambridge University Press 2016-01-25T21:40:41+00:00 http://www.cambridge.org/us/academic/subjects/mathematics/abstract-analysis/convergence-one-parameter-operator-semigroups-models-mathematical-biology-and-elsewhere?format=HB cshalizi in_NB books:noted mathematics analysis stochastic_processes markov_models biology re:almost_none https://pinboard.in/ https://pinboard.in/u:cshalizi/b:5cc9c7bc430f/ ERGODIC THEORETICAL APPROACH TO INVESTIGATE MEMORY PROPERTIES OF HEAVY TAILED PROCESSES 2016-01-07T02:22:45+00:00 https://7c4299fd-a-62cb3a1a-s-sites.googlegroups.com/site/takashiowada54/files/Dissertation_Main.pdf?attachauth=ANoY7cqLrOcqDFe9W9t4nEvuiM-VrXhW8Ko4OqTQ8cuaz9kdklj0ZnCfr8VIkyuL5vK4NxMU_4oTw153ueuTCwH8UpHADQgk9oSsrUyVWhhAagl7C2G0HandV-BxFBRniS3_543BGbeNYy8tOarMDnFgBBKIZnXdgIB66lWU9_6hzIpvVWmVgnkSAbFvNkzDF1GPhi4MuY2pL7Icnqv9Nbzakw1nKui0YbXoWHpIeiRurnQyE-I6nwY%3D&attredirects=0 cshalizito:NB stochastic_processes heavy_tails ergodic_theory long-range_dependence re:almost_none https://pinboard.in/ https://pinboard.in/u:cshalizi/b:18ab9ef7cfc9/ [1505.01163] Stationarity Tests for Time Series -- What Are We Really Testing? 2015-05-20T19:02:47+00:00 http://arxiv.org/abs/1505.01163 cshalizi time_series ergodic_theory statistics statistical_inference_for_stochastic_processes re:almost_none re:ADAfaEPoV in_NB have_read https://pinboard.in/ https://pinboard.in/u:cshalizi/b:f5a9d997df33/ Biased and Inefficient - What’s the right proof of the Continuous Mapping Theorem? 2015-05-12T13:58:36+00:00 http://notstatschat.tumblr.com/post/118005229311/whats-the-right-proof-of-the-continuous-mapping cshalizi probability funny:geeky re:almost_none lumley.thomas https://pinboard.in/ https://pinboard.in/u:cshalizi/b:4f89c50af624/ Trashorras , Wintenberger : Large deviations for bootstrapped empirical measures 2015-01-24T14:10:22+00:00 http://projecteuclid.org/euclid.bj/1411134447 cshalizi bootstrap empirical_processes large_deviations stochastic_processes statistics re:almost_none in_NB https://pinboard.in/ https://pinboard.in/u:cshalizi/b:d04a91aae66c/ Fischer : On the form of the large deviation rate function for the empirical measures of weakly interacting systems 2015-01-24T14:09:47+00:00 http://projecteuclid.org/euclid.bj/1411134444 cshalizi to:NB interacting_particle_systems large_deviations information_theory stochastic_processes to_read re:almost_none https://pinboard.in/ https://pinboard.in/u:cshalizi/b:b75d7b7ab849/ Probability in High Dimension 2014-07-09T13:26:22+00:00 https://www.princeton.edu/~rvan/ORF570.pdf cshalizi concentration_of_measure empirical_processes probability high-dimensional_probability learning_theory vc-dimension van_handel.ramon via:arsyed re:almost_none in_NB to_teach:childs_garden_of_statistical_learning_theory https://pinboard.in/ https://pinboard.in/u:cshalizi/b:f6d6eb8bb7ee/ Berkes , Liu , Wu : Komlós–Major–Tusnády approximation under dependence 2014-03-12T19:14:12+00:00 http://projecteuclid.org/euclid.aop/1393251303 cshalizi to:NB mixing ergodic_theory convergence_of_stochastic_processes central_limit_theorem stochastic_processes re:almost_none https://pinboard.in/ https://pinboard.in/u:cshalizi/b:e3a22f3fd2d2/ An Empirical Process Central Limit Theorem for Multidimensional Dependent Data - Springer 2014-03-10T15:11:23+00:00 http://link.springer.com/article/10.1007/s10959-012-0450-3?wt_mc=alerts.TOCjournals cshalizi empirical_processes stochastic_processes mixing re:almost_none in_NB https://pinboard.in/ https://pinboard.in/u:cshalizi/b:143a1f4e33e0/ [1309.3663] An Elementary Derivation of the Large Deviation Rate Function for Finite State Markov Chains 2013-09-17T20:37:02+00:00 http://arxiv.org/abs/1309.3663 cshalizi to:NB probability large_deviations stochastic_processes markov_models re:almost_none vidyasagar.mathukumalli https://pinboard.in/ https://pinboard.in/u:cshalizi/b:12f18e12f681/ Bayesian Representations of Stochastic Processes Under Learning: de Finetti Revisited (Jackson, Kalai and Smorodinsky, Econometrica 67 (1999): 875--893) 2013-09-09T03:34:37+00:00 http://www.jstor.org/discover/10.2307/2999460?uid=3739864&uid=2129&uid=2&uid=70&uid=4&uid=3739256&sid=21102630678603 cshalizi$\mu =\int_{\Theta}\mu _{\theta }d\lambda (\theta)$. Among these, a natural representation is one whose components $(\mu _{\theta}\text{'}{\rm s})$ are "learnable" (one can approximate μ θ by conditioning μ on observation of the process) and "sufficient for prediction" ($\mu _{\theta}\text{'}{\rm s}$ predictions are not aided by conditioning on observation of the process). We show the existence and uniqueness of such a representation under a suitable asymptotic mixing condition on the process. This representation can be obtained by conditioning on the tail-field of the process, and any learnable representation that is sufficient for prediction is asymptotically like the tail-field representation. This result is related to the celebrated de Finetti theorem, but with exchangeability weakened to an asymptotic mixing condition, and with his conclusion of a decomposition into i.i.d. component distributions weakened to components that are learnable and sufficient for prediction." - A bit astonishing there's no mention of de-Finetti-like theorems for partial exchangeability, or even of the ergodic decomposition.]]> stochastic_processes mixing learning_theory re:almost_none jackson.matthew_o. ergodic_theory re:pac-and-mar not_quite_scooped_exactly have_read in_NB https://pinboard.in/ https://pinboard.in/u:cshalizi/b:5645e3ec2909/ [1307.1565] Concentration inequalities for smooth random fields 2013-07-08T16:32:27+00:00 http://arxiv.org/abs/1307.1565 cshalizi random_fields empirical_processes concentration_of_measure stochastic_processes re:almost_none in_NB https://pinboard.in/ https://pinboard.in/u:cshalizi/b:145934415469/ [1306.3750] The Borel-Cantelli Lemma for Markov Sequences of Events 2013-06-18T14:27:02+00:00 http://arxiv.org/abs/1306.3750 cshalizi to:NB probability stochastic_processes re:almost_none https://pinboard.in/ https://pinboard.in/u:cshalizi/b:140952a3a6f8/ Moderate Deviations via Cumulants - Springer 2013-06-01T16:45:15+00:00 http://link.springer.com/article/10.1007/s10959-012-0437-0?wt_mc=alerts.TOCjournals.10959 cshalizi to:NB probability cumulants large_deviations re:almost_none https://pinboard.in/ https://pinboard.in/u:cshalizi/b:63ee72403c6a/