Imperial College London
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DrAdrianLeach

Faculty of Natural Sciences, Centre for Environmental Policy

Research Fellow
 
 
 
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Contact

 

+44 (0)1557 331 337a.w.leach

 
 
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Location

 

Silwood ParkSilwood Park

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Summary

 

Publications

Citation

BibTex format

@techreport{Romakkaniemi:2015,
author = {Romakkaniemi, A and Apostolidis, C and Bal, G and Froese, R and Kopra, J and Kuikka, S and Leach, A and Levontin, P and Mäntyniemi, S and Ó, Maoiléidigh N and Mumford, J and Pulkkinen, H and Rivot, E and Soni, V and Stergiou, K and White, J and Whitlock, R},
publisher = {ICES},
title = {Best practices for the provision of prior information for Bayesian stock assessment},
url = {http://ices.dk/news-and-events/news-archive/news/Pages/Report-published-on-prior-information-for-Bayesian-assessment.aspx},
year = {2015}
}
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RIS format (EndNote, RefMan)

TY  - RPRT
AB  - This manual represents a review of the potential sources and methods to be applied when providing prior information to Bayesian stock assessments and marine risk analysis.  The manual is compiled as a product of the EC Framework 7 ECOKNOWS project (www.ecoknows.eu).The manual begins by introducing the basic concepts of Bayesian inference and the role of prior information in the inference. Bayesian analysis is a mathematical formalization of a sequential learning process in a probabilistic rationale. Prior information (also called ”prior knowledge”, ”prior belief”, or simply a ”prior”) refers to any existing relevant knowledge available before the analysis of the newest observations (data) and the information included in them. Prior information is input to a Bayesian statistical analysis in the form of a probability distribution (a prior distribution) that summarizes beliefs about the parameter concerned in terms of relative support for different values.Apart from specifying probable parameter values, prior information also defines how the data are related to the phenomenon being studied, i.e. the model structure. Prior information should reflect the different degrees of knowledge about different parameters and the interrelationships among them.  Different sources of prior information are described as well as the particularities important for their successful utilization. The sources of prior information are classified into four main categories: (i) primary data, (ii) literature, (iii) online databases, and (iv) experts. This categorization is somewhat synthetic, but is useful for structuring the process of deriving a prior and for acknowledging different aspects of it.A hierarchy is proposed in which sources of prior information are ranked according to their proximity to the primary observations, so that use of raw data is preferred where possible. This hierarchy is reflected in the types of methods that might be suitable –
AU  - Romakkaniemi,A
AU  - Apostolidis,C
AU  - Bal,G
AU  - Froese,R
AU  - Kopra,J
AU  - Kuikka,S
AU  - Leach,A
AU  - Levontin,P
AU  - Mäntyniemi,S
AU  - Ó,Maoiléidigh N
AU  - Mumford,J
AU  - Pulkkinen,H
AU  - Rivot,E
AU  - Soni,V
AU  - Stergiou,K
AU  - White,J
AU  - Whitlock,R
PB  - ICES
PY  - 2015///
TI  - Best practices for the provision of prior information for Bayesian stock assessment
UR  - http://ices.dk/news-and-events/news-archive/news/Pages/Report-published-on-prior-information-for-Bayesian-assessment.aspx
ER  -
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