Probabilistic models for biological sequences (DNA and proteins) have many useful applications in bioinformatics. Normally, the values of parameters of these models have to be estimated from empirical data. However, even for the most common estimates, the maximum likelihood (ML) estimates, properties have not been completely explored. Here we assess the uniform accuracy of the ML estimates for models of several types: the independence model, the Markov chain and the hidden Markov model (HMM). Particularly, we derive rates of decay of the maximum estimation error by employing the measure concentration as well as the Gaussian approximation, and compare these rates.

中文翻译：

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生物序列概率模型最大似然估计的统一精度。


生物序列（DNA 和蛋白质）的概率模型在生物信息学中有许多有用的应用。通常，这些模型的参数值必须根据经验数据进行估计。然而，即使对于最常见的估计，即最大似然 (ML) 估计，其属性也尚未得到完全探索。在这里，我们评估几种类型模型的 ML 估计的统一精度：独立模型、马尔可夫链和隐马尔可夫模型 (HMM)。特别是，我们通过采用测量浓度以及高斯近似来推导最大估计误差的衰减率，并比较这些速率。