斯坦福&史密斯描述猪粪处理土壤产生的二氧化碳的非线性模型:最大熵先验

IF 0.6 4区 综合性期刊 Q3 MULTIDISCIPLINARY SCIENCES Acta Scientiarum-technology Pub Date : 2022-12-19 DOI:10.4025/actascitechnol.v45i1.56360
Edilson Marcelino Silva, S. A. Jane, F. A. Fernandes, Édipo Menezes da Silva, J. A. Muniz, T. J. Fernandes
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引用次数: 1

摘要

有机废物在土壤中的分解动力学可以用非线性回归模型来描述,然而,回归模型的理论对于足够大的样本是有效的,一般来说,在小样本中,这些特性是未知的。被广泛用于克服这一问题的数据分析方法之一是贝叶斯推理,因为它的优点是能够处理小样本,除了允许从以前的研究中合并信息外,甚至还具有参数的概率分布,因此,可以直接解释可信区间。然而,由于先验主观分布对后验分布的影响,批评已经提出。确定目标先验的一种方法是通过最大熵先验分布。对于有机废弃物在土壤中的分解数据,我们对这些参数的概率分布知之甚少。本研究的目的是利用最大熵先验分布的参数斯坦福&史密斯非线性模型。此外,利用模拟数据,了解先验分布的超参数对后验曲线的影响,并将该方法应用于猪粪土壤表层CO2矿化数据的描述。分析的数据来自实验室进行的一项实验,该实验评估了猪粪在土壤表面随时间的碳矿化。结果表明,最大熵先验贝叶斯方法可以有效地应用Stanford & Smith非线性模型对猪粪土壤表面碳矿化数据进行研究。
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Stanford & Smith nonlinear model in the description of CO2 evolved from soil treated with swine manure: maximum entropy prior
The dynamics of organic waste decomposition in the soil can be described by nonlinear regression models, however, the theory for regression models is valid for sufficiently large samples, and in general, in small samples, these properties are unknown. One of the methods for data analysis that has been widely used to overcome this problem is the bayesian inference, as it has the advantage of being able to work with small samples, in addition to allowing the incorporation of information from previous studies, and even having a probability distribution for the parameters, consequently, to present a direct interpretation for the credibility interval. However, criticism has been made because of the effect that a prior subjective distribution can have on posterior distribution. One way of determining objective prior is through of maximum entropy prior distributions. For data of organic waste decomposition in the soil, little is known about the probability distributions of the parameters. The present study aimed to use of maximum entropy prior distributions to the parameters of the Stanford & Smith nonlinear model. In addition, using simulated data, to understand the effect that hyperparameters of prior distribution has on the posterior curve, and also to apply the methodology in the description of CO2 mineralization data from swine manure applied to the soil surface. Data analyzed came from an experiment conducted in a laboratory that evaluated the carbon mineralization of swine manure on the soil surface over time. The posterior distributions were obtained, so the bayesian methodology with maximum entropy prior was efficient in the study of the Stanford & Smith nonlinear model to the data of carbon mineralization of swine manure on the soil surface.
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来源期刊
Acta Scientiarum-technology
Acta Scientiarum-technology 综合性期刊-综合性期刊
CiteScore
1.40
自引率
12.50%
发文量
60
审稿时长
6-12 weeks
期刊介绍: The journal publishes original articles in all areas of Technology, including: Engineerings, Physics, Chemistry, Mathematics, Statistics, Geosciences and Computation Sciences. To establish the public inscription of knowledge and its preservation; To publish results of research comprising ideas and new scientific suggestions; To publicize worldwide information and knowledge produced by the scientific community; To speech the process of scientific communication in Technology.
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