2a:null 2b:["$","title",null,{"children":"von Mises distribution | Mattias Villani | Observable"}] 31:[] 2e:{"id":"f01a7daa73a030c5","slug":"von-mises-distribution","title":"von Mises distribution","thumbnail":"bfd1f4383e69f29b8ff18e0994fa0fcbbcf8c12b751bb2d08ad65a740a1972cc","access_level":"public","create_time":"$D2023-06-04T19:51:11.635Z","update_time":"$D2023-09-04T09:21:32.418Z","view_time":null,"first_public_time":"$D2023-06-04T19:51:11.635Z","fork":{"id":"1d97c9ee6e70964c","slug":"split-normal-distribution","title":"Split-Normal distribution","owner":{"id":"7592a7ab121f74a0","login":"mattiasvillani","name":"Mattias Villani"}},"stars":0,"starred":false,"owner":{"type":"team","id":"7592a7ab121f74a0","login":"mattiasvillani","name":"Mattias Villani","avatar_url":"https://avatars-next.observableusercontent.com/a440aaf39a3abefb86cbcc758fd7a5ab42555ae68d6825b52ec006f385a94d1d","tier":"pro_2024"},"version":1323,"publish_version":null,"role":null,"authored":false,"authors":[{"type":"individual","id":"56499be8cf43f0ef","login":"mattiasvillani","name":"Mattias Villani","avatar_url":"https://avatars-next.observableusercontent.com/a440aaf39a3abefb86cbcc758fd7a5ab42555ae68d6825b52ec006f385a94d1d","tier":null}],"members":[{"type":"individual","id":"56499be8cf43f0ef","login":"mattiasvillani","name":"Mattias Villani","avatar_url":"https://avatars-next.observableusercontent.com/a440aaf39a3abefb86cbcc758fd7a5ab42555ae68d6825b52ec006f385a94d1d","tier":null,"role":"editor"}],"comments":[],"custom_thumbnail":false,"trashed":false,"subscribed":null,"forks":1,"collection":{"id":"40c8ef5af1a51aff","slug":"distributions","title":"Statistical distributions","owner":{"id":"7592a7ab121f74a0","login":"mattiasvillani","name":"Mattias Villani"}},"type":"notebook","body":{"title":"von Mises distribution","cells":[{"id":38,"value":"# von Mises distribution","pinned":false,"hidden":false,"mode":"md"},{"id":629,"value":"

${tex`X\\sim \\mathrm{vonMises}(\\mu, \\kappa)`}, for ${tex`-\\pi d.x <= quantile, x: \"x\", y: \"pdf\", fill: \"steelblue\", opacity: 0.2})\n ]\n })","pinned":false,"hidden":false,"mode":"ojs"},{"id":109,"value":"
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\n\n---\n## 🙈 Appendix 🙈\n\nHere is some code and data details that you probably do not want to read.","pinned":false,"hidden":false,"mode":"md"},{"id":221,"value":"#### Imports","pinned":false,"hidden":false,"mode":"md"},{"id":26,"value":"jstat = require('jstat')","pinned":false,"hidden":false,"mode":"ojs"},{"id":750,"value":"mathjs = require('mathjs')","pinned":false,"hidden":false,"mode":"ojs"},{"id":1173,"value":"besselfunc = require(\"bessel@1.0.2/bessel.js\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":223,"value":"#### Data","pinned":false,"hidden":false,"mode":"md"},{"id":654,"value":"mu = params[0]","pinned":false,"hidden":false,"mode":"ojs"},{"id":656,"value":"kappa = params[1]","pinned":false,"hidden":false,"mode":"ojs"},{"id":662,"value":"quantile = params[2]","pinned":false,"hidden":false,"mode":"ojs"},{"id":706,"value":"mean = mu","pinned":false,"hidden":false,"mode":"ojs"},{"id":766,"value":"variance = 1 - besselfunc.besseli(kappa, 1) / besselfunc.besseli(kappa, 0)","pinned":false,"hidden":false,"mode":"ojs"},{"id":1199,"value":"gridsize = 0.01","pinned":false,"hidden":false,"mode":"ojs"},{"id":830,"value":"function vonmisespdf(x, mu, kappa) {\n return Math.exp(kappa * Math.cos(x - mu)) /\n (2 * Math.PI * besselfunc.besseli(kappa, 0));\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":845,"value":"function vonmisescdf(quantile, mu, kappa){\n return d3.sum(pdfdata.filter(d => d.x <=quantile).map(d => d.pdf))*gridsize\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":17,"value":"pdfdata = {\n const x = d3.range(-Math.PI, Math.PI, gridsize);\n const pdf = x.map((x) => ({ x: x, pdf: vonmisespdf(x, mu, kappa) }));\n return pdf;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":19,"value":"cdf = vonmisescdf(quantile, mu, kappa)","pinned":false,"hidden":false,"mode":"ojs"},{"id":1234,"value":"// not used currently\ncirclepdfdata = {\n const anglegrid = d3.range(-Math.PI, Math.PI, 0.01)\n const angledata = anglegrid.map(angle => ({x: Math.cos(angle), y: Math.sin(angle), pdf: vonmisespdf(angle, mu, kappa), constpdf: 1/(2*Math.PI), zeropdf: 0}))\n return angledata \n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":229,"value":"#### Style","pinned":false,"hidden":false,"mode":"md"},{"id":202,"value":"","pinned":false,"hidden":false,"mode":"html"},{"id":58,"value":"viewof md_colour = Inputs.color({label: \"Highlight color cells\", value: \"#F5F5DC\"})","pinned":false,"hidden":false,"mode":"ojs"}],"files":"$W31","stdlib":"1","titling":"auto"}} 2f:"$2e:body"