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recent bookmarks from hallucigeniaUn garçon pas comme les autres (Bayes) - Priors for the parameters in a Gaussian process2022-09-30T14:21:17+00:00
https://dansblog.netlify.app/posts/2022-09-07-priors5/priors5.html#moving-beyond-the-mat%C3%A9rn
hallucigeniaIf you’re not a machine learner (and sometimes if you are), Gaussian processes need priors on their parameters. Like everything else to do with Gaussian processes, this can be delicate. This post works through some options.]]>statistics:gaussian_processes statistics:spatial statistics:gams statistics:bayesianhttps://pinboard.in/u:hallucigenia/b:3d53be92447d/Random effects and penalized splines are the same thing - Higher Order Functions2022-07-27T13:01:42+00:00
https://www.tjmahr.com/random-effects-penalized-splines-same-thing/
hallucigeniaWeighted wiggles and smoothed categories]]>statistics:hierarchical statistics:gams regression to_teachhttps://pinboard.in/u:hallucigenia/b:9243c311d2f3/What is the kernel trick? Why is it important? | by Grace Zhang | Medium2022-06-28T15:51:41+00:00
https://medium.com/@zxr.nju/what-is-the-kernel-trick-why-is-it-important-98a98db0961d
hallucigeniaWhen talking about kernels in machine learning, most likely the first thing that comes into your mind is the support vector machines (SVM) model because the kernel trick is widely used in the SVM…]]>machine_learning statistics:high_dimensional statistics:gams statistics:clustering teaching:statistics tutorialhttps://pinboard.in/u:hallucigenia/b:abfeb696c34a/The plsmselect package2021-04-27T16:31:14+00:00
https://cran.r-project.org/web/packages/plsmselect/vignettes/plsmselect.html
hallucigenia
Generalized Additive Models (GAMs) are characterized by a linear predictor that can be broken down into a sum of a linear term (the XβXβ term) and a smooth term (the ∑jfj(xj)∑jfj(xj) term). The goal of plsmselect is to provide a flexible interface for parameter estimation under various penalty structures on the linear parameters ββ (“none”, ℓ1ℓ1, or ℓ2ℓ2) and the smooth functions fjfj’s (ℓ1ℓ1 or ℓ2ℓ2). Most noteworthy, the package allows users to fit so called GAMLASSO models that a...]]>R_packages LASSO statistics:regression statistics:gams smoothing_and_penalization to_tryhttps://pinboard.in/u:hallucigenia/b:0bab3c1014ce/Robust Gaussian Process Modeling2020-10-02T18:50:56+00:00
https://betanalpha.github.io/assets/case_studies/gaussian_processes.html#4_Advanced_Topics
hallucigeniagaussian_processes statistics:gams statistics:hierarchical to_teach tutorialhttps://pinboard.in/u:hallucigenia/b:28e36d9c0045/Gaussian Markov Random Fields: Theory and Applications2018-06-04T17:27:25+00:00
https://books.google.ca/books?id=TLBYs-faw-0C&lpg=PP1&dq=Gaussian%20Markov%20Random%20Fields%3A%20Theory%20and%20Applications&pg=PP1#v=onepage&q&f=false
hallucigeniaBooks to_read gaussian_processes graph_theory math:dynamical_systems statistics:additive_models statistics:bayesian statistics:regression statistics:gams statistics:networkshttps://pinboard.in/https://pinboard.in/u:hallucigenia/b:e8568d2f5ba0/Regression: Models, Methods and Applications - Ludwig Fahrmeir, Thomas Kneib, Stefan Lang, Brian Marx - Google Books2018-02-04T15:24:02+00:00
https://books.google.ca/books?hl=en&lr=&id=EQxU9iJtipAC&oi=fnd&pg=PT18&dq=%09+Regression:+Models,+Methods+and+Applications&ots=YfZz8fsArb&sig=KunLRn3F8OAlJ4TMwQGFPoaej64#v=onepage&q&f=false
hallucigeniaThe aim of this book is an applied and unified introduction into parametric, non- and semiparametric regression that closes the gap between theory and application. The most important models and methods in regression are presented on a solid formal basis, and their appropriate application is shown through many real data examples and case studies. Availability of (user-friendly) software has been a major criterion for the methods selected and presented. Thus, the book primarily targets...]]>regression statistics:regression books teaching:statistics statistics:gams statistics:spatial to_readhttps://pinboard.in/u:hallucigenia/b:3f8d2a854a90/mgcViz: visual tools for GAMs2017-12-19T18:15:18+00:00
https://github.com/mfasiolo/mgcViz
hallucigeniaR R_packages visualization statistics:gams to_try ggplot2 githubhttps://pinboard.in/https://pinboard.in/u:hallucigenia/b:708552d6d973/VAST: Spatio-temporal analysis of univariate or multivariate data, e.g., standardizing data for multiple species or stage2017-09-26T16:44:11+00:00
https://github.com/James-Thorson/VAST
hallucigeniastatistics:gams statistics:time_series statistics:fisheries fisheries fisheries:methods statistics:bayesian statistics:spatial R_packageshttps://pinboard.in/https://pinboard.in/u:hallucigenia/b:0b4eb5081375/R help - Use pcls in "mgcv" package to achieve constrained cubic spline2017-04-05T14:16:50+00:00
http://r.789695.n4.nabble.com/Use-pcls-in-quot-mgcv-quot-package-to-achieve-constrained-cubic-spline-td4660966.html
hallucigeniaUse pcls in "mgcv" package to achieve constrained cubic spline. Hello everyone, Dr. wood told me that I can adapting his example to force cubic spline to pass through certain...]]>statistics:gams statistics:regression statistics:constraints advice mgcvhttps://pinboard.in/u:hallucigenia/b:482a6ce857a8/