计算机科学
贝叶斯概率
频数推理
贝叶斯统计
多级模型
人工智能
贝叶斯推理
自然语言处理
机器学习
作者
Ladislas Nalborczyk,Cédric Batailler,Hélène Lœvenbruck,Anne Vilain,Paul - Christian Bürkner
出处
期刊:Journal of Speech Language and Hearing Research
[American Speech-Language-Hearing Association]
日期:2019-05-21
卷期号:62 (5): 1225-1242
被引量:93
标识
DOI:10.1044/2018_jslhr-s-18-0006
摘要
Purpose Bayesian multilevel models are increasingly used to overcome the limitations of frequentist approaches in the analysis of complex structured data. This tutorial introduces Bayesian multilevel modeling for the specific analysis of speech data, using the brms package developed in R. Method In this tutorial, we provide a practical introduction to Bayesian multilevel modeling by reanalyzing a phonetic data set containing formant (F1 and F2) values for 5 vowels of standard Indonesian (ISO 639-3:ind), as spoken by 8 speakers (4 females and 4 males), with several repetitions of each vowel. Results We first give an introductory overview of the Bayesian framework and multilevel modeling. We then show how Bayesian multilevel models can be fitted using the probabilistic programming language Stan and the R package brms, which provides an intuitive formula syntax. Conclusions Through this tutorial, we demonstrate some of the advantages of the Bayesian framework for statistical modeling and provide a detailed case study, with complete source code for full reproducibility of the analyses ( https://osf.io/dpzcb /). Supplemental Material https://doi.org/10.23641/asha.7973822
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