Representation and estimation of stochastic populations
Representation and estimation of stochastic populations
复制标题
随机总体的表示和估计
DOI：
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发表时间：
2015
期刊：
影响因子：
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通讯作者：
J. Houssineau
中科院分区：
文献类型：
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作者：
J. Houssineau
这项工作关注的是由不确定和可变数量的个体组成的群体的表示和估计，这些个体可以随时间随机进化。解决这类问题的现有解决方案假设所有个体都是可区分的，或者没有个体是可区分的。换句话说，重点要么是特定的个人，要么是整个人口。这些方法具有互补的优点和缺点，本工作的主要目的是为部分不可区分的种群引入合适的表示。为了实现这一目标，必须研究一种足够通用的方法来量化不同类型的不确定性。结果表明，这可以在测量理论贝叶斯范式中实现。所提出的随机总体表示然后用于引入各种过滤算法，从最一般的到最具体的。然后在不同的情况下演示了其中一种滤波器的建模可能性和准确性。
This work is concerned with the representation and the estimation of populations composed of an uncertain and varying number of individuals which can randomly evolve in time. The existing solutions that address this type of problems make the assumption that all or none of the individuals are distinguishable. In other words, the focus is either on specific individuals or on the population as a whole. These approaches have complimentary advantages and drawbacks and the main objective in this work is to introduce a suitable representation for partially-indistinguishable populations. In order to fulfil this objective, a sufficiently versatile way of quantifying different types of uncertainties has to be studied. It is demonstrated that this can be achieved within a measure-theoretic Bayesian paradigm. The proposed representation of stochastic populations is then used for the introduction of various filtering algorithms from the most general to the most specific. The modelling possibilities and the accuracy of one of these filters are then demonstrated in different situations.