Bayesian estimation in forest surveys when samples or prior information are fuzzy

doi:10.1016/0165-0114(95)00088-7

Fuzzy Sets and Systems

Volume 77, Issue 3, 12 February 1996, Pages 277-290

https://doi.org/10.1016/0165-0114(95)00088-7 Get rights and content

Abstract

Fuzzy phenomena are frequently encountered in forestry due to the complexity of the systems under investigation. In this paper, existing inference methods are presented to accommodate situations in which random events take place in fuzzy circumstances. Fuzzy concepts are incorporated into these commonly accepted approaches to account for uncertainties used by factors other than randomness which traditional statistical approaches deal primarily with. Based on two extensions of likelihood functions for fuzzy samples, Bayesian estimates are generalized for use when sample information and prior distribution of parameters are fuzzy. The estimators are developed for some typical membership functions for fuzzy samples and fuzzy priors. Applications are illustrated using examples for forestry.

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Citation Excerpt :
The above proposition is very useful for further discussions. The topic on fuzzy estimators has been studied by many researchers (Dubois & Prade, 1986; Gertner & Zhu, 1996; Gil, Corral, & Gil, 1985; Kruse & Meyer, 1987; Schnatter, 1992, Schnatter, 1993). Gebhardt, Gil, and Kruse (1998) gave a good review in this topic.
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Bayesian estimation in forest surveys when samples or prior information are fuzzy

Abstract

European J. Oper. Res.

Statistical Decision Theory and Bayesian Analysis

Application of empirical Bayes/James-Stein procedures to simultaneous estimation problems in forest inventory

Forest Sci.

Constructing membership functions using statistical data

Fuzzy sets and Systems

The minimum inaccuracy fuzzy estimation: an extension of the maximum likelihood principle

Stochastics

Statistical decision theory and its application to forest engineering

J. Forestry

Localizing a diameter increment model with a sequential Bayesian procedure

Forest Sci.

The use of Bayes/empirical Bayes estimation in individual tree volume equation development

Forest Sci.

Empirical Bayes development of Honduran pinc yield models

Forest Sci.

The spaces of fuzzy probability and possibility