Pinboard (cshalizi) https://pinboard.in/u:cshalizi/public/ recent bookmarks from cshalizi [2504.03503] Operator Learning: A Statistical Perspective 2025-04-28T01:23:12+00:00 https://arxiv.org/abs/2504.03503 cshalizi to:NB approximation tewari.ambuj statistics equations_of_motion_from_a_time_series https://pinboard.in/ https://pinboard.in/u:cshalizi/b:ad7e303203c2/ [2502.12063] Low-Rank Thinning 2025-02-22T22:42:53+00:00 https://arxiv.org/abs/2502.12063 cshalizi to:NB computational_statistics low-rank_approximation kernel_methods mackey.lester via:mraginsky approximation https://pinboard.in/ https://pinboard.in/u:cshalizi/b:6f021998692f/ LOWER BOUNDS FOR APPROXIMATION BY NONLINEAR MANIFOLDS 2024-12-11T16:01:58+00:00 https://www.ams.org/journals/tran/1968-133-01/S0002-9947-1968-0226281-1/S0002-9947-1968-0226281-1.pdf cshalizito:NB geometry approximation re:codename:catherine_wheel https://pinboard.in/ https://pinboard.in/u:cshalizi/b:f3f9aa2eb087/ [2311.03910] Structure of universal formulas 2023-12-08T19:04:39+00:00 https://arxiv.org/abs/2311.03910 cshalizi to:NB learning_theory neural_networks approximation https://pinboard.in/ https://pinboard.in/u:cshalizi/b:43e65eb23777/ [1901.06230] Computing large market equilibria using abstractions 2023-06-06T01:55:01+00:00 https://arxiv.org/abs/1901.06230 cshalizi to_read economics optimization approximation re:in_soviet_union_optimization_problem_solves_you in_NB https://pinboard.in/ https://pinboard.in/u:cshalizi/b:af36dc73f9c7/ [2305.00322] Toward $L_infty$-recovery of Nonlinear Functions: A Polynomial Sample Complexity Bound for Gaussian Random Fields 2023-05-05T01:47:16+00:00 https://arxiv.org/abs/2305.00322 cshalizi approximation learning_theory random_fields in_NB https://pinboard.in/ https://pinboard.in/u:cshalizi/b:fb73aea45ac1/ [2101.04653] Benchmarking Simulation-Based Inference 2021-11-22T15:53:03+00:00 https://arxiv.org/abs/2101.04653 cshalizi to:NB approximation re:codename:catherine_wheel to_read via:rvenkat https://pinboard.in/ https://pinboard.in/u:cshalizi/b:a1b66545b031/ [2107.07562] On universal approximation and error bounds for Fourier Neural Operators 2021-08-14T00:48:29+00:00 https://arxiv.org/abs/2107.07562 cshalizi neural_networks approximation fourier_analysis https://pinboard.in/ https://pinboard.in/u:cshalizi/b:8fec631c17c6/ [2107.10885] Laplace and Saddlepoint Approximations in High Dimensions 2021-07-26T15:00:11+00:00 https://arxiv.org/abs/2107.10885 cshalizi high-dimensional_statistics approximation reid nancy laplace_approximation statistics in_NB https://pinboard.in/ https://pinboard.in/u:cshalizi/b:4441b47f2d45/ [2107.03975] Compressibility Analysis of Asymptotically Mean Stationary Processes 2021-07-11T05:55:06+00:00 https://arxiv.org/abs/2107.03975 cshalizi to:NB stochastic_processes sparsity approximation ergodic_theory https://pinboard.in/ https://pinboard.in/u:cshalizi/b:485fccb044e3/ [2105.14368] Fit without fear: remarkable mathematical phenomena of deep learning through the prism of interpolation 2021-06-01T13:33:06+00:00 https://arxiv.org/abs/2105.14368 cshalizi approximation neural_networks learning_theory belkin.mikhail statistics have_read adversarial_examples computational_statistics kernel_methods in_NB interpolation_aka_memorizing_the_training_data https://pinboard.in/ https://pinboard.in/u:cshalizi/b:d46689212c03/ Automated Solution of Heterogeneous Agent Models | David Childers 2021-05-10T19:59:53+00:00 https://donskerclass.github.io/publication/automatedsolution/ cshalizi to:NB approximation agent-based_models economics numerical_analysis childers.david https://pinboard.in/ https://pinboard.in/u:cshalizi/b:d956218cfb06/ [2103.03191] Generalization Bounds for Sparse Random Feature Expansions 2021-04-21T14:53:40+00:00 https://arxiv.org/abs/2103.03191 cshalizi sparsity random_features approximation learning_theory to_teach:childs_garden_of_statistical_learning_theory in_NB https://pinboard.in/ https://pinboard.in/u:cshalizi/b:dde4a10d084c/ [2005.10224] The Random Feature Model for Input-Output Maps between Banach Spaces 2021-04-20T13:21:08+00:00 https://arxiv.org/abs/2005.10224 cshalizi random_features analysis via:rvenkat approximation in_NB have_read dynamical_systems re:codename:catherine_wheel https://pinboard.in/ https://pinboard.in/u:cshalizi/b:81171133ef9a/ [2101.12353] On the capacity of deep generative networks for approximating distributions 2021-02-04T17:39:08+00:00 https://arxiv.org/abs/2101.12353 cshalizi to:NB approximation probability neural_networks https://pinboard.in/ https://pinboard.in/u:cshalizi/b:fdece6d93814/ [2101.11968] Reproducing kernel Hilbert spaces, polynomials and the classical moment problems 2021-02-04T17:38:12+00:00 https://arxiv.org/abs/2101.11968 cshalizi to:NB probability approximation hilbert_space kernel_methods re:codename:catherine_wheel https://pinboard.in/ https://pinboard.in/u:cshalizi/b:bd951012e765/ [1912.06987] The Generalization Error of the Minimum-norm Solutions for Over-parameterized Neural Networks 2021-02-04T15:34:29+00:00 https://arxiv.org/abs/1912.06987 cshalizi approximation learning_theory random_features in_NB https://pinboard.in/ https://pinboard.in/u:cshalizi/b:9ff53c05a88d/ [1801.03437] Approximation beats concentration? An approximation view on inference with smooth radial kernels 2021-01-23T03:43:38+00:00 https://arxiv.org/abs/1801.03437 cshalizi to:NB kernel_methods hilbert_space approximation learning_theory belkin.mikhail to_teach:childs_garden_of_statistical_learning_theory https://pinboard.in/ https://pinboard.in/u:cshalizi/b:6eebabf098c0/ [2101.05873] Data-driven learning for the Mori--Zwanzig formalism: a generalization of the Koopman learning framework 2021-01-19T20:57:23+00:00 https://arxiv.org/abs/2101.05873 cshalizi macro_from_micro stochastic_processes approximation in_NB mori-zwanzig koopman_operators https://pinboard.in/ https://pinboard.in/u:cshalizi/b:5c04db814345/ [2012.13424] Homogeneous Interpretable Approximations to Heterogeneous SIR Models 2021-01-14T15:51:02+00:00 https://arxiv.org/abs/2012.13424 cshalizi to:NB epidemic_models approximation https://pinboard.in/ https://pinboard.in/u:cshalizi/b:551b235d42e3/ Katzfuss , Guinness : A General Framework for Vecchia Approximations of Gaussian Processes 2020-12-21T14:09:14+00:00 https://projecteuclid.org/euclid.ss/1608541222 cshalizi to:NB approximation computational_statistics random_fields gaussian_processes stochastic_processes statistics_on_manifolds https://pinboard.in/ https://pinboard.in/u:cshalizi/b:21d74c2d020c/ [2012.08496] Spectral Methods for Data Science: A Statistical Perspective 2020-12-16T15:13:22+00:00 https://arxiv.org/abs/2012.08496 cshalizi to:NB statistics spectral_methods data_mining approximation linear_algebra to_teach:data-mining https://pinboard.in/ https://pinboard.in/u:cshalizi/b:6c3188aa8c89/ [1506.02785] On the Error of Random Fourier Features 2020-12-14T01:48:11+00:00 https://arxiv.org/abs/1506.02785 cshalizi random_features kernel_methods approximation computational_statistics concentration_of_measure two-sample_tests regression schneider.jeff have_read to_teach:childs_garden_of_statistical_learning_theory in_NB https://pinboard.in/ https://pinboard.in/u:cshalizi/b:bdadd4e91fb9/ [2011.10985] A universal probability approximation method: Markov process approach 2020-11-30T04:02:00+00:00 https://arxiv.org/abs/2011.10985 cshalizi markov_models convergence_of_stochastic_processes stochastic_processes stochastic_differential_equations approximation stochastic_gradient_descent in_NB https://pinboard.in/ https://pinboard.in/u:cshalizi/b:c3a1ccd774e2/ [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/ [2005.04751] Tractable nonlinear memory functions as a tool to capture and explain dynamical behaviours 2020-11-15T21:15:24+00:00 https://arxiv.org/abs/2005.04751 cshalizi to:NB dynamical_systems approximation networks biochemical_networks non-equilibrium https://pinboard.in/ https://pinboard.in/u:cshalizi/b:3dff9992307a/ [1106.6184] Non-perturbative heterogeneous mean-field approach to epidemic spreading in complex networks 2020-07-29T15:28:00+00:00 https://arxiv.org/abs/1106.6184 cshalizi to:NB approximation epidemics_on_networks re:do-institutions-evolve https://pinboard.in/ https://pinboard.in/u:cshalizi/b:a1dcc8726d6b/ Approximating data - ScienceDirect 2020-05-16T18:10:40+00:00 https://www.sciencedirect.com/science/article/abs/pii/S1226319208000380 cshalizi to:NB approximation statistics foundations_of_statistics davies.p.l. color_me_skeptical https://pinboard.in/ https://pinboard.in/u:cshalizi/b:d16415803a00/ Approximating data with weighted smoothing splines: Journal of Nonparametric Statistics: Vol 20, No 3 2020-05-16T18:09:43+00:00 https://www.tandfonline.com/doi/abs/10.1080/10485250801948625 cshalizi to:NB smoothing approximation splines statistics davies.p.l. https://pinboard.in/ https://pinboard.in/u:cshalizi/b:c2207876cd87/ [math/0606591] Approximation of stationary processes by Hidden Markov Models 2020-05-12T23:48:53+00:00 https://arxiv.org/abs/math/0606591 cshalizi to:NB approximation stochastic_processes information_theory markov_models re:AoS_project to_read https://pinboard.in/ https://pinboard.in/u:cshalizi/b:7ded2a998ee1/ Nothing to see here… move along (regression discontinuity edition) | Models Of Reality 2020-01-12T22:06:57+00:00 http://models.street-artists.org/2020/01/09/nothing-to-see-here-move-along-regression-discontinuity-edition/ cshalizi approximation causal_inference bad_data_analysis statistics via:gelman regression regression_discontinuity https://pinboard.in/ https://pinboard.in/u:cshalizi/b:d70c2ae82bc4/ [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/ [1711.10414] When are epsilon-nets small? 2019-08-08T13:06:47+00:00 https://arxiv.org/abs/1711.10414 cshalizi to:NB learning_theory approximation to_teach:childs_garden_of_statistical_learning_theory https://pinboard.in/ https://pinboard.in/u:cshalizi/b:e712cdb4888a/ [1906.09282] Robustness of Dynamical Quantities of Interest via Goal-Oriented Information Theory 2019-08-05T13:45:12+00:00 https://arxiv.org/abs/1906.09282 cshalizi to:NB information_theory probability stochastic_processes approximation https://pinboard.in/ https://pinboard.in/u:cshalizi/b:56c3e1c3e7fc/ [1906.05746] Nonlinear System Identification via Tensor Completion 2019-06-14T11:40:07+00:00 https://arxiv.org/abs/1906.05746 cshalizi to:NB approximation tensors regression computational_statistics statistics https://pinboard.in/ https://pinboard.in/u:cshalizi/b:7ff988587750/ [1905.12122] Deep Learning Moment Closure Approximations using Dynamic Boltzmann Distributions 2019-05-30T16:14:07+00:00 https://arxiv.org/abs/1905.12122 cshalizi to:NB approximation stochastic_processes mjolsness.eric moment_closures https://pinboard.in/ https://pinboard.in/u:cshalizi/b:84e8d18a231e/ [1905.10409] Greedy Shallow Networks: A New Approach for Constructing and Training Neural Networks 2019-05-28T17:40:53+00:00 https://arxiv.org/abs/1905.10409 cshalizi to:NB learning_theory approximation neural_networks your_favorite_deep_neural_network_sucks https://pinboard.in/ https://pinboard.in/u:cshalizi/b:99b556b19030/ [1904.00687] On the Power and Limitations of Random Features for Understanding Neural Networks 2019-05-10T03:24:29+00:00 https://arxiv.org/abs/1904.00687 cshalizi to:NB neural_networks learning_theory optimization approximation your_favorite_deep_neural_network_sucks to_teach:childs_garden_of_statistical_learning_theory random_features https://pinboard.in/ https://pinboard.in/u:cshalizi/b:dfb4828711a7/ A Spline Theory of Deep Learning 2019-04-29T17:48:57+00:00 http://proceedings.mlr.press/v80/balestriero18b.html cshalizi to:NB to_read approximation splines neural_networks machine_learning your_favorite_deep_neural_network_sucks via:csantos https://pinboard.in/ https://pinboard.in/u:cshalizi/b:9b98002244e0/ [1904.03673] Every Local Minimum is a Global Minimum of an Induced Model 2019-04-11T00:08:43+00:00 https://arxiv.org/abs/1904.03673 cshalizi to:NB optimization approximation neural_networks kaelbling.leslie_pack https://pinboard.in/ https://pinboard.in/u:cshalizi/b:e8419d151be9/ [1904.02505] A deterministic and computable Bernstein-von Mises theorem 2019-04-08T21:52:23+00:00 https://arxiv.org/abs/1904.02505 cshalizi statistics approximation laplace_approximation probability in_NB https://pinboard.in/ https://pinboard.in/u:cshalizi/b:53a67d69a1cf/ The Lasserre Hierarch in Approximation Algorithms 2019-04-08T19:15:16+00:00 https://sites.math.washington.edu/~rothvoss/lecturenotes/lasserresurvey.pdf cshalizi to:NB to_read approximation optimization re:in_soviet_union_optimization_problem_solves_you https://pinboard.in/ https://pinboard.in/u:cshalizi/b:7e7dfef7ce00/ [1612.07545] A Revisit of Hashing Algorithms for Approximate Nearest Neighbor Search 2019-01-05T15:11:58+00:00 https://arxiv.org/abs/1612.07545 cshalizi data_mining approximation nearest_neighbors locality-sensitive_hashing hashing have_read via:vaguery random_projections k-means databases in_NB https://pinboard.in/ https://pinboard.in/u:cshalizi/b:6e194513aac5/ Parameters as interacting particles: long time convergence and asymptotic error scaling of neural networks 2018-12-12T01:59:50+00:00 http://papers.nips.cc/paper/7945-parameters-as-interacting-particles-long-time-convergence-and-asymptotic-error-scaling-of-neural-networks cshalizi to:NB to_read neural_networks approximation learning_theory via:rvenkat https://pinboard.in/ https://pinboard.in/u:cshalizi/b:42b892a61018/ Weighted Sums of Random Kitchen Sinks: Replacing minimization with randomization in learning 2018-09-13T19:40:00+00:00 https://papers.nips.cc/paper/3495-weighted-sums-of-random-kitchen-sinks-replacing-minimization-with-randomization-in-learning cshalizi approximation kernel_methods random_projections statistics prediction classifiers rahimi.ali recht.benjamin machine_learning have_read in_NB https://pinboard.in/ https://pinboard.in/u:cshalizi/b:9d11b9bca403/ [1806.06850] Polynomial Regression As an Alternative to Neural Nets 2018-07-05T13:06:38+00:00 https://arxiv.org/abs/1806.06850 cshalizi to:NB your_favorite_deep_neural_network_sucks regression neural_networks statistics matloff.norman approximation computational_statistics have_read https://pinboard.in/ https://pinboard.in/u:cshalizi/b:4138e391c13c/ Multivariate approximation | Numerical analysis | Cambridge University Press 2018-06-23T16:40:46+00:00 http://www.cambridge.org/us/academic/subjects/mathematics/numerical-analysis/multivariate-approximation?format=HB&WT.mc_id=LFA-MAT-CL-CMACM%2Bseries#vFK9KkeLHpmYPUkP.97 cshalizi to:NB approximation mathematics books:noted https://pinboard.in/ https://pinboard.in/u:cshalizi/b:14f5244484ab/ One Parameter Is Always Enough 2018-05-29T15:01:35+00:00 https://colala.bcs.rochester.edu/papers/piantadosi2018one.pdf cshalizi model_selection approximation dynamical_systems chaos have_read to:blog https://pinboard.in/ https://pinboard.in/u:cshalizi/b:89db40e79b3b/ As If — Kwame Anthony Appiah | Harvard University Press 2017-09-07T16:03:33+00:00 http://www.hup.harvard.edu/catalog.php?isbn=9780674975002 cshalizi books:noted philosophy epistemology philosophy_of_science approximation modeling idealization in_NB https://pinboard.in/ https://pinboard.in/u:cshalizi/b:d992f3c0970c/ A Novel Algorithm for Coarse-Graining of Cellular Automata - Springer 2016-12-09T16:11:04+00:00 http://link.springer.com/chapter/10.1007/978-3-319-11520-7_27 cshalizi to:NB macro_from_micro approximation cellular_automata via:? https://pinboard.in/ https://pinboard.in/u:cshalizi/b:b8c0580f7c68/ [1611.06168] On $p$-values 2016-12-04T18:43:08+00:00 https://arxiv.org/abs/1611.06168 cshalizi to:NB statistics misspecification approximation p-values hypothesis_testing via:vaguery https://pinboard.in/ https://pinboard.in/u:cshalizi/b:e527ca5df465/ A Universal Approximation Theorem for Mixture-of-Experts Models | Neural Computation | MIT Press Journals 2016-11-23T18:22:31+00:00 http://www.mitpressjournals.org/doi/abs/10.1162/NECO_a_00892#.WDXdn3eZNR0 cshalizi to:NB ensemble_methods statistics graphical_models approximation https://pinboard.in/ https://pinboard.in/u:cshalizi/b:9633ee3eda5c/ [1508.05906] Chaining, Interpolation, and Convexity 2016-09-07T14:53:37+00:00 http://arxiv.org/abs/1508.05906 cshalizi empirical_processes learning_theory approximation convexity functional_analysis van_handel.ramon in_NB to_teach:childs_garden_of_statistical_learning_theory https://pinboard.in/ https://pinboard.in/u:cshalizi/b:724678402496/ Approximation Methods in Probability Theory | Vydas Čekanavičius | Springer 2016-07-08T15:19:22+00:00 http://www.springer.com/us/book/9783319340715 cshalizi to:NB probability approximation mathematics convergence_of_stochastic_processes https://pinboard.in/ https://pinboard.in/u:cshalizi/b:ed9139eebdd4/ A Single Hidden Layer Feedforward Network with Only One Neuron in the Hidden Layer Can Approximate Any Univariate Function 2016-06-28T17:54:23+00:00 http://www.mitpressjournals.org/doi/abs/10.1162/NECO_a_00849 cshalizi to:NB to_read neural_networks approximation https://pinboard.in/ https://pinboard.in/u:cshalizi/b:ef55378a6207/ [1601.00670] Variational Inference: A Review for Statisticians 2016-02-09T03:02:29+00:00 http://arxiv.org/abs/1601.00670 cshalizi to:NB variational_inference statistics computational_statistics approximation blei.david https://pinboard.in/ https://pinboard.in/u:cshalizi/b:a48bd78ff1e2/ Importance Sampling Over Sets: A New Probabilistic Inference Scheme 2015-07-15T14:15:21+00:00 http://auai.org/uai2015/proceedings/papers/143.pdf cshalizi to:NB computational_statistics monte_carlo statistics approximation re:fitness_sampling https://pinboard.in/ https://pinboard.in/u:cshalizi/b:da2b78d00cf4/ Random Features for Large-Scale Kernel Machines 2015-07-15T14:07:00+00:00 http://papers.nips.cc/paper/3182-random-features-for-large-scale-kernel-machines cshalizi machine_learning computational_statistics approximation fourier_analysis kernel_methods statistics re:hyperbolic_networks in_NB have_read random_features https://pinboard.in/ https://pinboard.in/u:cshalizi/b:dfa4fe79abe7/ [1404.1578] Models as Approximations: How Random Predictors and Model Violations Invalidate Classical Inference in Regression 2015-02-01T13:43:35+00:00 http://arxiv.org/abs/1404.1578 cshalizi have_read statistics regression linear_regression bootstrap misspecification estimation approximation in_NB https://pinboard.in/ https://pinboard.in/u:cshalizi/b:f929c523e284/ Dehling , Durieu , Tusche : Approximating class approach for empirical processes of dependent sequences indexed by functions 2015-01-24T14:13:31+00:00 http://projecteuclid.org/euclid.bj/1402488943 cshalizi empirical_processes approximation stochastic_processes markov_models dynamical_systems ergodic_theory mixing in_NB https://pinboard.in/ https://pinboard.in/u:cshalizi/b:b57eb1847383/ Fully Exponential Laplace Approximations to Expectations and Variances of Nonpositive Functions (Tierny, Kass and Kadane, 1989) 2015-01-24T00:36:04+00:00 http://www.stat.cmu.edu/~kass/papers/nonlinearFunctions.pdf cshalizi in_NB have_read laplace_approximation statistics approximation kith_and_kin kass.robert re:HEAS https://pinboard.in/ https://pinboard.in/u:cshalizi/b:46598bd52c5d/ [1405.0058] Underestimating extreme events in power-law behavior due to machine-dependent cutoffs 2015-01-23T13:45:39+00:00 http://arxiv.org/abs/1405.0058 cshalizi to:NB to_read heavy_tails approximation computational_statistics have_skimmed https://pinboard.in/ https://pinboard.in/u:cshalizi/b:3a0596bb0021/ Orders of Infinity by G. H. Hardy - Free Ebook 2014-10-21T22:32:05+00:00 http://www.gutenberg.org/ebooks/38079 cshalizito:NB books:recommended mathematics analysis asymptotics approximation hardy.g.h. have_skimmed via:teddy_s. https://pinboard.in/ https://pinboard.in/u:cshalizi/b:819925514c0d/ A Primer on Regression Splines 2014-05-02T15:27:58+00:00 http://cran.r-project.org/web/packages/crs/vignettes/spline_primer.pdf cshalizi splines nonparametrics regression approximation statistics computational_statistics racine.jeffrey_s. to_teach:statcomp to_teach:undergrad-ADA have_read in_NB https://pinboard.in/ https://pinboard.in/u:cshalizi/b:c6d32a51d6f0/ Distortion of genealogical properties when the sample is very large 2014-03-11T21:00:33+00:00 http://www.pnas.org/content/111/6/2385.abstract cshalizi to:NB stochastic_processes approximation historical_genetics statistics https://pinboard.in/ https://pinboard.in/u:cshalizi/b:06eb14984d8a/ [1402.1694] Asymptotically Exact MCMC Algorithms via Local Approximations of Computationally Intensive Models 2014-02-21T00:28:45+00:00 http://arxiv.org/abs/1402.1694 cshalizi to:NB monte_carlo statistics approximation https://pinboard.in/ https://pinboard.in/u:cshalizi/b:b733c3ea2120/ [1311.7286] Approximate Bayesian Computation with composite score functions 2013-12-16T15:22:23+00:00 http://arxiv.org/abs/1311.7286 cshalizi to:NB approximation likelihood statistics estimation https://pinboard.in/ https://pinboard.in/u:cshalizi/b:2557276541a9/ [1310.5543] Universalities of Reproducing Kernels Revisited 2013-10-23T14:10:33+00:00 http://arxiv.org/abs/1310.5543 cshalizi to:NB kernel_methods hilbert_space approximation learning_theory https://pinboard.in/ https://pinboard.in/u:cshalizi/b:c450e5f20ae1/ [1212.6906] Central Limit Theorems and Multiplier Bootstrap when p is much larger than n 2013-09-09T03:36:11+00:00 http://arxiv.org/abs/1212.6906 cshalizi to:NB empirical_processes stochastic_processes approximation central_limit_theorem bootstrap statistics probability https://pinboard.in/ https://pinboard.in/u:cshalizi/b:038f7e73d1e5/ Bryant, J. and Sangwin, C.: How Round Is Your Circle? Where Engineering and Mathematics Meet. 2013-05-10T21:29:58+00:00 http://press.princeton.edu/titles/8624.html cshalizi books:noted approximation geometry engineering modeling to_teach books:owned https://pinboard.in/ https://pinboard.in/u:cshalizi/b:9b2ded06b1c9/