Sample size for maximum-likelihood estimates of Gaussian model depending on dimensionality of pattern space
作者：
Highlights：
• The paper attempts to answer the question - What is the indicative pattern size required to estimate the parameters of the Gaussian model with the defined accuracy?
• Obtained results provide useful recommendations for researchers working on pattern statistical models.
• The recommended values of the sample size are easy to remember.
• We assume that presented results could aid in applications wherein statistical pattern modeling and statistical pattern recognition based on Gaussian modeling are involved.
摘要
•The paper attempts to answer the question - What is the indicative pattern size required to estimate the parameters of the Gaussian model with the defined accuracy?•Obtained results provide useful recommendations for researchers working on pattern statistical models.•The recommended values of the sample size are easy to remember.•We assume that presented results could aid in applications wherein statistical pattern modeling and statistical pattern recognition based on Gaussian modeling are involved.
论文关键词：Maximum-likelihood estimate,Likelihood function,Gaussian model,Gaussian mixture model,Sample size,Dimensionality,Pattern space,Heteroscedastic data.
论文评审过程：Received 24 January 2018, Revised 14 December 2018, Accepted 21 January 2019, Available online 31 January 2019, Version of Record 19 February 2019.
论文官网地址：https://doi.org/10.1016/j.patcog.2019.01.046
