{"id":4435,"date":"2023-05-16T08:05:44","date_gmt":"2023-05-16T08:05:44","guid":{"rendered":"https:\/\/isipta23.sipta.org\/?page_id=4435"},"modified":"2023-07-11T12:47:09","modified_gmt":"2023-07-11T12:47:09","slug":"long-martin","status":"publish","type":"page","link":"https:\/\/isipta23.sipta.org\/accepted-papers\/long-martin\/","title":{"rendered":"Long Martin"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-page\" data-elementor-id=\"4435\" class=\"elementor elementor-4435\" data-elementor-post-type=\"page\">\n\t\t\t\t\t\t\t\t\t<section data-eae-particle=\"{\n  &quot;particles&quot;: {\n    &quot;number&quot;: {\n      &quot;value&quot;: 80,\n      &quot;density&quot;: {\n        &quot;enable&quot;: true,\n        &quot;value_area&quot;: 800\n      }\n    },\n    &quot;color&quot;: {\n      &quot;value&quot;: &quot;#ffffff&quot;\n    },\n    &quot;shape&quot;: {\n      &quot;type&quot;: &quot;circle&quot;,\n      &quot;stroke&quot;: {\n        &quot;width&quot;: 0,\n      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data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;,&quot;shape_divider_bottom&quot;:&quot;wave-brush&quot;}\">\n\t\t\t\t\t\t\t<div class=\"elementor-background-overlay\"><\/div>\n\t\t\t\t\t\t<div class=\"elementor-shape elementor-shape-bottom\" data-negative=\"false\">\n\t\t\t<svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" viewBox=\"0 0 283.5 27.8\" preserveAspectRatio=\"none\">\n\t<path class=\"elementor-shape-fill\" d=\"M283.5,9.7c0,0-7.3,4.3-14,4.6c-6.8,0.3-12.6,0-20.9-1.5c-11.3-2-33.1-10.1-44.7-5.7\ts-12.1,4.6-18,7.4c-6.6,3.2-20,9.6-36.6,9.3C131.6,23.5,99.5,7.2,86.3,8c-1.4,0.1-6.6,0.8-10.5,2c-3.8,1.2-9.4,3.8-17,4.7\tc-3.2,0.4-8.3,1.1-14.2,0.9c-1.5-0.1-6.3-0.4-12-1.6c-5.7-1.2-11-3.1-15.8-3.7C6.5,9.2,0,10.8,0,10.8V0h283.5V9.7z M260.8,11.3\tc-0.7-1-2-0.4-4.3-0.4c-2.3,0-6.1-1.2-5.8-1.1c0.3,0.1,3.1,1.5,6,1.9C259.7,12.2,261.4,12.3,260.8,11.3z 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elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-cd1ef36 elementor-widget elementor-widget-image\" data-id=\"cd1ef36\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.16.0 - 20-09-2023 *\/\n.elementor-widget-image{text-align:center}.elementor-widget-image a{display:inline-block}.elementor-widget-image a img[src$=\".svg\"]{width:48px}.elementor-widget-image img{vertical-align:middle;display:inline-block}<\/style>\t\t\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" loading=\"lazy\" width=\"1024\" height=\"128\" src=\"https:\/\/isipta23.sipta.org\/wp-content\/uploads\/2022\/08\/LogoISIPTA23-long-white-1024x128.png\" class=\"attachment-large size-large wp-image-546\" alt=\"LogoISIPTA23-long-white\" srcset=\"https:\/\/isipta23.sipta.org\/wp-content\/uploads\/2022\/08\/LogoISIPTA23-long-white-1024x128.png 1024w, 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100%;-webkit-mask-repeat:var(--divider-pattern-repeat);mask-repeat:var(--divider-pattern-repeat);background-color:var(--divider-color);-webkit-mask-image:var(--divider-pattern-url);mask-image:var(--divider-pattern-url)}.elementor-widget-divider--no-spacing{--divider-pattern-size:auto}.elementor-widget-divider--bg-round{--divider-pattern-repeat:round}.rtl .elementor-widget-divider .elementor-divider__text{direction:rtl}.e-con-inner>.elementor-widget-divider,.e-con>.elementor-widget-divider{width:var(--container-widget-width,100%);--flex-grow:var(--container-widget-flex-grow)}<\/style>\t\t<div class=\"elementor-divider\">\n\t\t\t<span class=\"elementor-divider-separator\">\n\t\t\t\t\t\t<\/span>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-9adf215 elementor-widget elementor-widget-heading\" data-id=\"9adf215\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.16.0 - 20-09-2023 *\/\n.elementor-heading-title{padding:0;margin:0;line-height:1}.elementor-widget-heading .elementor-heading-title[class*=elementor-size-]>a{color:inherit;font-size:inherit;line-height:inherit}.elementor-widget-heading .elementor-heading-title.elementor-size-small{font-size:15px}.elementor-widget-heading .elementor-heading-title.elementor-size-medium{font-size:19px}.elementor-widget-heading .elementor-heading-title.elementor-size-large{font-size:29px}.elementor-widget-heading .elementor-heading-title.elementor-size-xl{font-size:39px}.elementor-widget-heading .elementor-heading-title.elementor-size-xxl{font-size:59px}<\/style><h2 class=\"elementor-heading-title elementor-size-default\">Martin<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-fac39ee elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"fac39ee\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-daa0aae\" data-id=\"daa0aae\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-b4d9d71 elementor-widget elementor-widget-text-editor\" data-id=\"b4d9d71\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.16.0 - 20-09-2023 *\/\n.elementor-widget-text-editor.elementor-drop-cap-view-stacked .elementor-drop-cap{background-color:#69727d;color:#fff}.elementor-widget-text-editor.elementor-drop-cap-view-framed .elementor-drop-cap{color:#69727d;border:3px solid;background-color:transparent}.elementor-widget-text-editor:not(.elementor-drop-cap-view-default) .elementor-drop-cap{margin-top:8px}.elementor-widget-text-editor:not(.elementor-drop-cap-view-default) .elementor-drop-cap-letter{width:1em;height:1em}.elementor-widget-text-editor .elementor-drop-cap{float:left;text-align:center;line-height:1;font-size:50px}.elementor-widget-text-editor .elementor-drop-cap-letter{display:inline-block}<\/style>\t\t\t\t<a class=\"boton-volver\" href=\"javascript:history.back()\">\u2aa1 Back<\/a>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-99180d5 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"99180d5\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-1cf27c4\" data-id=\"1cf27c4\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-63d4f60 elementor-widget elementor-widget-heading\" data-id=\"63d4f60\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h1 class=\"elementor-heading-title elementor-size-default\">Fiducial inference viewed through a possibility-theoretic inferential model lens<\/h1>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-56b42ba elementor-widget elementor-widget-heading\" data-id=\"56b42ba\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<span class=\"elementor-heading-title elementor-size-default\">Fisher's fiducial argument is widely viewed as a failed version of Neyman's theory of confidence limits.  But Fisher's goal---Bayesian-like probabilistic uncertainty quantification without priors---was more ambitious than Neyman's, and it's not out of reach.  I've recently shown that reliable, prior-free probabilistic uncertainty quantification must be grounded in the theory of imprecise probability, and I've put forward a possibility-theoretic solution that achieves it.  This has been met with resistance, however, in part due to the statistical community's singular focus on confidence limits.  Indeed, if imprecision isn't needed to answer confidence-limit-related questions, then what's the point?  In this paper, for a class of practically useful models, I explain specifically why the fiducial argument gives valid confidence limits, i.e., it's the \"best probabilistic approximation\" of the possibilistic solution I recently advanced.  This sheds new light on what the fiducial argument is doing and on what's lost in terms of reliability when imprecision is ignored and the fiducial argument is pushed for more than just confidence limits.<\/span>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-35ccd2d elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"35ccd2d\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-33 elementor-top-column elementor-element elementor-element-105e301\" data-id=\"105e301\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-aeb8d65 oew-align-center elementor-widget elementor-widget-oew-button-effects\" data-id=\"aeb8d65\" data-element_type=\"widget\" data-widget_type=\"oew-button-effects.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\r\n\r\n\t\t<div class=\"oew-button oew-btn-effect-30\">\r\n\t\t\t<a href=\"https:\/\/isipta23.sipta.org\/wp-content\/uploads\/2023\/06\/martin23a.pdf\" class=\"oew-link\">\r\n\t\t\t\t\r\n\t\t\t\t<span>PAPER<\/span>\r\n\r\n\t\t\t\t\t\t\t<\/a>\r\n\t\t<\/div>\r\n\r\n\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<div class=\"elementor-column elementor-col-33 elementor-top-column elementor-element elementor-element-e41c979\" data-id=\"e41c979\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-d656a65 oew-align-center elementor-widget elementor-widget-oew-button-effects\" data-id=\"d656a65\" data-element_type=\"widget\" data-widget_type=\"oew-button-effects.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\r\n\r\n\t\t<div class=\"oew-button oew-btn-effect-30\">\r\n\t\t\t<a href=\"https:\/\/isipta23.sipta.org\/wp-content\/uploads\/2023\/07\/presmartin.pdf\" class=\"oew-link\">\r\n\t\t\t\t\r\n\t\t\t\t<span>PRESENTATION<\/span>\r\n\r\n\t\t\t\t\t\t\t<\/a>\r\n\t\t<\/div>\r\n\r\n\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<div class=\"elementor-column elementor-col-33 elementor-top-column elementor-element elementor-element-c2414e8\" data-id=\"c2414e8\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-83db414 oew-align-center elementor-widget elementor-widget-oew-button-effects\" data-id=\"83db414\" data-element_type=\"widget\" data-widget_type=\"oew-button-effects.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\r\n\r\n\t\t<div class=\"oew-button oew-btn-effect-30\">\r\n\t\t\t<a href=\"https:\/\/isipta23.sipta.org\/wp-content\/uploads\/2023\/07\/postermartin.pdf\" class=\"oew-link\">\r\n\t\t\t\t\r\n\t\t\t\t<span>poster<\/span>\r\n\r\n\t\t\t\t\t\t\t<\/a>\r\n\t\t<\/div>\r\n\r\n\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-dd2b88d elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"dd2b88d\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-e2b8390\" data-id=\"e2b8390\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-e9aa4b4 elementor-widget elementor-widget-text-editor\" data-id=\"e9aa4b4\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<a class=\"boton-volver\" href=\"javascript:history.back()\">\u2aa1 Back<\/a>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"<p>Martin \u2aa1 Back Fiducial inference viewed through a possibility-theoretic inferential model lens Fisher&#8217;s fiducial argument is widely viewed as a failed version of Neyman&#8217;s theory of confidence limits. But Fisher&#8217;s goal&#8212;Bayesian-like probabilistic uncertainty quantification without priors&#8212;was more ambitious than Neyman&#8217;s, and it&#8217;s not out of reach. I&#8217;ve recently shown that reliable, prior-free probabilistic uncertainty quantification [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":3821,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"elementor_header_footer","meta":{"footnotes":""},"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v21.2 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Long Martin - ISIPTA 2023<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/isipta23.sipta.org\/accepted-papers\/long-martin\/\" \/>\n<meta property=\"og:locale\" content=\"en_GB\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Long Martin - ISIPTA 2023\" \/>\n<meta property=\"og:description\" content=\"Martin \u2aa1 Back Fiducial inference viewed through a possibility-theoretic inferential model lens Fisher&#8217;s fiducial argument is widely viewed as a failed version of Neyman&#8217;s theory of confidence limits. But Fisher&#8217;s goal&#8212;Bayesian-like probabilistic uncertainty quantification without priors&#8212;was more ambitious than Neyman&#8217;s, and it&#8217;s not out of reach. I&#8217;ve recently shown that reliable, prior-free probabilistic uncertainty quantification [&hellip;]\" \/>\n<meta property=\"og:url\" content=\"https:\/\/isipta23.sipta.org\/accepted-papers\/long-martin\/\" \/>\n<meta property=\"og:site_name\" content=\"ISIPTA 2023\" \/>\n<meta property=\"article:modified_time\" content=\"2023-07-11T12:47:09+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/isipta23.sipta.org\/wp-content\/uploads\/2022\/08\/LogoISIPTA23-long-white-1024x128.png\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Estimated reading time\" \/>\n\t<meta name=\"twitter:data1\" content=\"1 minute\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\/\/isipta23.sipta.org\/accepted-papers\/long-martin\/\",\"url\":\"https:\/\/isipta23.sipta.org\/accepted-papers\/long-martin\/\",\"name\":\"Long Martin - ISIPTA 2023\",\"isPartOf\":{\"@id\":\"https:\/\/isipta23.sipta.org\/#website\"},\"datePublished\":\"2023-05-16T08:05:44+00:00\",\"dateModified\":\"2023-07-11T12:47:09+00:00\",\"breadcrumb\":{\"@id\":\"https:\/\/isipta23.sipta.org\/accepted-papers\/long-martin\/#breadcrumb\"},\"inLanguage\":\"en-GB\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\/\/isipta23.sipta.org\/accepted-papers\/long-martin\/\"]}]},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\/\/isipta23.sipta.org\/accepted-papers\/long-martin\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Portada\",\"item\":\"https:\/\/isipta23.sipta.org\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Accepted Papers\",\"item\":\"https:\/\/isipta23.sipta.org\/accepted-papers\/\"},{\"@type\":\"ListItem\",\"position\":3,\"name\":\"Long Martin\"}]},{\"@type\":\"WebSite\",\"@id\":\"https:\/\/isipta23.sipta.org\/#website\",\"url\":\"https:\/\/isipta23.sipta.org\/\",\"name\":\"ISIPTA 2023\",\"description\":\"The International Symposium on Imprecise Probabilities\",\"publisher\":{\"@id\":\"https:\/\/isipta23.sipta.org\/#organization\"},\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\/\/isipta23.sipta.org\/?s={search_term_string}\"},\"query-input\":\"required name=search_term_string\"}],\"inLanguage\":\"en-GB\"},{\"@type\":\"Organization\",\"@id\":\"https:\/\/isipta23.sipta.org\/#organization\",\"name\":\"ISIPTA 2023\",\"url\":\"https:\/\/isipta23.sipta.org\/\",\"logo\":{\"@type\":\"ImageObject\",\"inLanguage\":\"en-GB\",\"@id\":\"https:\/\/isipta23.sipta.org\/#\/schema\/logo\/image\/\",\"url\":\"https:\/\/isipta23.sipta.org\/wp-content\/uploads\/2022\/08\/cropped-LogoISIPTA23-white.png\",\"contentUrl\":\"https:\/\/isipta23.sipta.org\/wp-content\/uploads\/2022\/08\/cropped-LogoISIPTA23-white.png\",\"width\":500,\"height\":500,\"caption\":\"ISIPTA 2023\"},\"image\":{\"@id\":\"https:\/\/isipta23.sipta.org\/#\/schema\/logo\/image\/\"}}]}<\/script>\n<!-- \/ Yoast SEO plugin. -->","yoast_head_json":{"title":"Long Martin - ISIPTA 2023","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/isipta23.sipta.org\/accepted-papers\/long-martin\/","og_locale":"en_GB","og_type":"article","og_title":"Long Martin - ISIPTA 2023","og_description":"Martin \u2aa1 Back Fiducial inference viewed through a possibility-theoretic inferential model lens Fisher&#8217;s fiducial argument is widely viewed as a failed version of Neyman&#8217;s theory of confidence limits. But Fisher&#8217;s goal&#8212;Bayesian-like probabilistic uncertainty quantification without priors&#8212;was more ambitious than Neyman&#8217;s, and it&#8217;s not out of reach. 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