很多生態(tài)系統(tǒng)都有混沌的或近乎混沌的動態(tài),。在這種情況下,，難以驗證相關(guān)數(shù)據(jù)是否符合具體模型,，因為噪音使得與模型進行統(tǒng)計對比不可能進行?，F(xiàn)在,，Simon Wood設(shè)計了一個進行這種推斷的統(tǒng)計模型，其所依據(jù)的是從原始數(shù)據(jù)提取對相位變化不敏感的匯總統(tǒng)計,，并與從該模型模擬出的數(shù)據(jù)進行對比,。研究人員通過對一個著名問題的應(yīng)用演示了該方法，這個問題是：John Nicholson關(guān)于銅綠蠅(Lucilia cuprina)種群規(guī)模的經(jīng)典生態(tài)實驗中的周期的性質(zhì),。（生物谷Bioon.com）
生物谷推薦原文出處：
Nature doi:10.1038/nature09319
Statistical inference for noisy nonlinear ecological dynamic systems
Simon N. Wood
Chaotic ecological dynamic systems defy conventional statistical analysis. Systems with near-chaotic dynamics are little better. Such systems are almost invariably driven by endogenous dynamic processes plus demographic and environmental process noise, and are only observable with error. Their sensitivity to history means that minute changes in the driving noise realization, or the system parameters, will cause drastic changes in the system trajectory1. This sensitivity is inherited and amplified by the joint probability density of the observable data and the process noise, rendering it useless as the basis for obtaining measures of statistical fit. Because the joint density is the basis for the fit measures used by all conventional statistical methods2, this is a major theoretical shortcoming. The inability to make well-founded statistical inferences about biological dynamic models in the chaotic and near-chaotic regimes, other than on an ad hoc basis, leaves dynamic theory without the methods of quantitative validation that are essential tools in the rest of biological science. Here I show that this impasse can be resolved in a simple and general manner, using a method that requires only the ability to simulate the observed data on a system from the dynamic model about which inferences are required. The raw data series are reduced to phase-insensitive summary statistics, quantifying local dynamic structure and the distribution of observations. Simulation is used to obtain the mean and the covariance matrix of the statistics, given model parameters, allowing the construction of a ‘synthetic likelihood’ that assesses model fit. This likelihood can be explored using a straightforward Markov chain Monte Carlo sampler, but one further post-processing step returns pure likelihood-based inference. I apply the method to establish the dynamic nature of the fluctuations in Nicholson’s classic blowfly experiments3, 4, 5.
