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Register a new userBayesian Classification and Inference in a Probabilistic Type Theory with Records
التصنيف والاستدلال البيئي في نظرية نوع الاحتمالية مع السجلات
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نقترح حساب احتمامي للتناسم الدلالي والتصنيف المصنوع من حيث نظرية النوع الاحتمالية مع السجلات، والبناء على كوبر وآخرون.آل.(2014) وكوبر وآخرون.آل.(2015).نقترح أن نقترح تركيبات نظرية من النوع الاحتمالية من أسلاك بايس، وشبكات بايزيان.العنصر المركزي في هذه المنشآت هو نسخة نظرية من النوع من متغير عشوائي.نوضح هذا الحساب مع لعبة لغة بسيطة تجمع بين التصنيف الاحتمالي للمدخلات الإحصائية مع الاستدلال الاحتمالية (الدلالي).
We propose a probabilistic account of semantic inference and classification formulated in terms of probabilistic type theory with records, building on Cooper et. al. (2014) and Cooper et. al. (2015). We suggest probabilistic type theoretic formulations of Naive Bayes Classifiers and Bayesian Networks. A central element of these constructions is a type-theoretic version of a random variable. We illustrate this account with a simple language game combining probabilistic classification of perceptual input with probabilistic (semantic) inference.
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Starting from an existing account of semantic classification and learning from interaction formulated in a Probabilistic Type Theory with Records, encompassing Bayesian inference and learning with a frequentist flavour, we observe some problems with
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