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首页> 外文期刊>Journal of the royal statistical society >Estimating finite mixtures of semi-Markov chains: an application to the segmentation of temporal sensory data
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Estimating finite mixtures of semi-Markov chains: an application to the segmentation of temporal sensory data

机译：估计半马尔可夫链的有限混合：在时间感觉数据分割中的应用

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摘要

In food science, it is of great interest to obtain information about the temporal perception of aliments to create new products, to modify existing products or more generally to understand the mechanisms of perception. Temporal dominance of sensations is a technique to measure temporal perception which consists in choosing sequentially attributes describing a food product over tasting. This work introduces new statistical models based on finite mixtures of semi-Markov chains to describe data collected with the temporal dominance of sensations protocol, allowing different temporal perceptions for a same product within a population. The identifiability of the parameters of such mixture models is discussed. Sojourn time distributions are fitted with a gamma probability distribution and a penalty is added to the log-likelihood to ensure convergence of the expectation-maximization algorithm to a non-degenerate solution. Information criteria are employed for determining the number of mixture components. Then, the individual qualitative trajectories are clustered with the help of the maximum a posteriori probability approach. A simulation study confirms the good behaviour of the estimation procedure proposed. The methodology is illustrated on an example of consumers' perception of a Gouda cheese and assesses the existence of several behaviours in terms of perception of this product.
机译：在食品科学中，获得有关饮食的时间感知的信息非常有趣，这些信息可用于创建新产品，修改现有产品，或更笼统地理解感知机制。感觉的时间优势是一种测量时间感觉的技术，该技术包括依次选择描述食品而不是品尝的属性。这项工作引入了基于半马尔可夫链的有限混合的新统计模型，以描述以感觉主导的时间优势协议收集的数据，从而允许总体中同一产品具有不同的时间感知。讨论了这种混合模型的参数可识别性。逗留时间分布与伽马概率分布拟合，并且对数似然增加了惩罚，以确保期望最大化算法收敛到非退化解。信息标准用于确定混合物组分的数量。然后，借助最大后验概率方法将各个定性轨迹聚类。仿真研究证实了所提出的估计程序的良好行为。该方法以消费者对Gouda奶酪的感知为例进行了说明，并根据对该产品的感知评估了几种行为的存在。

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