{"id":5643,"date":"2026-06-11T13:53:22","date_gmt":"2026-06-11T17:53:22","guid":{"rendered":"https:\/\/precrisa.ca\/?p=5643"},"modified":"2026-06-11T13:54:58","modified_gmt":"2026-06-11T17:54:58","slug":"webinaire-en-rediffusion-introduction-aux-statistiques-bayesiennes","status":"publish","type":"post","link":"https:\/\/precrisa.ca\/en\/nouvelles\/webinaire-en-rediffusion-introduction-aux-statistiques-bayesiennes\/","title":{"rendered":"Webinaire en rediffusion | Introduction aux statistiques bay\u00e9siennes"},"content":{"rendered":"<p>Vous avez manqu\u00e9 notre midi-webinaire consacr\u00e9 aux statistiques bay\u00e9siennes? Bonne nouvelle : l&#8217;enregistrement de la pr\u00e9sentation est maintenant disponible <a href=\"https:\/\/precrisa.ca\/en\/outils-numeriques\/\" data-type=\"link\" data-id=\"https:\/\/precrisa.ca\/outils-numeriques\/\">en rediffusion sur le site du R\u00e9seau Pr\u00e9crisa<\/a>.<\/p>\n\n\n\n<p>Pr\u00e9sent\u00e9 le 17 mars 2026 par&nbsp;<strong>Samuel M\u00e9rineau<\/strong>, candidat au doctorat en psychologie \u00e0 l&#8217;Universit\u00e9 de Montr\u00e9al, ce webinaire proposait une introduction accessible aux principes fondamentaux des statistiques bay\u00e9siennes et \u00e0 leur utilisation dans la recherche.<\/p>\n\n\n\n<p>Alors que les approches fr\u00e9quentistes demeurent largement dominantes dans les formations universitaires et les publications scientifiques, les statistiques bay\u00e9siennes suscitent un int\u00e9r\u00eat croissant dans plusieurs disciplines.<\/p>\n\n\n\n<p>Elles offrent notamment la possibilit\u00e9 d&#8217;int\u00e9grer des connaissances pr\u00e9alables aux analyses et proposent une fa\u00e7on diff\u00e9rente d&#8217;interpr\u00e9ter les probabilit\u00e9s, particuli\u00e8rement pertinente dans l&#8217;\u00e9tude de ph\u00e9nom\u00e8nes complexes, uniques ou caract\u00e9ris\u00e9s par l&#8217;incertitude.<\/p>\n\n\n\n<p>Au cours de cette pr\u00e9sentation, Samuel M\u00e9rineau a abord\u00e9 :<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>les fondements des statistiques bay\u00e9siennes;<\/li>\n\n\n\n<li>la construction et l&#8217;utilisation des croyances a priori;<\/li>\n\n\n\n<li>le th\u00e9or\u00e8me de Bayes;<\/li>\n\n\n\n<li>l&#8217;application des concepts de base dans des analyses simples et plus complexes;<\/li>\n\n\n\n<li>l&#8217;utilisation des logiciels R et JAGS dans le cadre d&#8217;un court atelier pratique.<\/li>\n<\/ul>\n\n\n\n<p>Cette activit\u00e9 s&#8217;adressait \u00e0 toute personne souhaitant s&#8217;initier aux statistiques bay\u00e9siennes, qu&#8217;elle provienne du milieu de la recherche, de la pratique ou des \u00e9tudes sup\u00e9rieures.<\/p>\n\n\n\n<p>La rediffusion est maintenant disponible sur la page suivante\u00a0: <a href=\"https:\/\/precrisa.ca\/en\/outils-numeriques\/\">Outils num\u00e9riques &#8211; Pr\u00e9crisa<\/a>.<\/p>","protected":false},"excerpt":{"rendered":"<p>Vous avez manqu\u00e9 notre midi-webinaire consacr\u00e9 aux statistiques bay\u00e9siennes? Bonne nouvelle : l&#8217;enregistrement de la pr\u00e9sentation est maintenant disponible en rediffusion sur le site du R\u00e9seau Pr\u00e9crisa.<\/p>","protected":false},"author":1,"featured_media":5648,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_seopress_titles_title":"","_seopress_titles_desc":"","_seopress_robots_index":"","_seopress_robots_follow":"","_seopress_robots_imageindex":"","_seopress_robots_snippet":"","_seopress_robots_primary_cat":"","_seopress_robots_breadcrumbs":"","_seopress_robots_freeze_modified_date":"","_seopress_robots_custom_modified_date":"","_seopress_robots_canonical":"","_seopress_social_fb_title":"","_seopress_social_fb_desc":"","_seopress_social_fb_img":"","_seopress_social_fb_img_attachment_id":5648,"_seopress_social_fb_img_width":2103,"_seopress_social_fb_img_height":1200,"_seopress_social_twitter_title":"","_seopress_social_twitter_desc":"","_seopress_social_twitter_img":"https:\/\/precrisa.ca\/wp-content\/uploads\/2024\/08\/precrisa-logo.jpg","_seopress_social_twitter_img_attachment_id":2654,"_seopress_social_twitter_img_width":1200,"_seopress_social_twitter_img_height":630,"_seopress_redirections_value":"","_seopress_redirections_enabled":"","_seopress_redirections_enabled_regex":"","_seopress_redirections_logged_status":"","_seopress_redirections_param":"","_seopress_redirections_type":0,"_seopress_analysis_target_kw":"","_et_pb_use_builder":"off","_et_pb_old_content":"","_et_gb_content_width":"","_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[2],"tags":[],"class_list":["post-5643","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-general"],"jetpack_featured_media_url":"https:\/\/precrisa.ca\/wp-content\/uploads\/2026\/06\/introduction-aux-statistiques-bayesiennes.jpg","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/precrisa.ca\/en\/wp-json\/wp\/v2\/posts\/5643","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/precrisa.ca\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/precrisa.ca\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/precrisa.ca\/en\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/precrisa.ca\/en\/wp-json\/wp\/v2\/comments?post=5643"}],"version-history":[{"count":1,"href":"https:\/\/precrisa.ca\/en\/wp-json\/wp\/v2\/posts\/5643\/revisions"}],"predecessor-version":[{"id":5645,"href":"https:\/\/precrisa.ca\/en\/wp-json\/wp\/v2\/posts\/5643\/revisions\/5645"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/precrisa.ca\/en\/wp-json\/wp\/v2\/media\/5648"}],"wp:attachment":[{"href":"https:\/\/precrisa.ca\/en\/wp-json\/wp\/v2\/media?parent=5643"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/precrisa.ca\/en\/wp-json\/wp\/v2\/categories?post=5643"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/precrisa.ca\/en\/wp-json\/wp\/v2\/tags?post=5643"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}