Identifying good condition in native vegetation: A Bayesian regression and decision theoretic approach

MODSIM05 - International Congress on Modelling and Simulation: Advances and Applications for Management and Decision Making, Proceedings

Volumn , Issue , 2005, Pages 435-441

  Low Choy, S J ^a   Stewart Koster, B ^a,c   Kelly, A ^b   Eyre, T ^b   Mengersen, K M ^a  

^a QUEENSLAND UNIVERSITY OF TECHNOLOGY   (Australia)

^b DEPARTMENT OF ENVIRONMENT AND RESOURCE MANAGEMENT   (Australia)

^c CSIRO   (Australia)

Author keywords

Bayesian decision framework; Benchmarking; Disturbance; Indicators; Native vegetation condition; Poisson Gamma regression; Pressure state response; Regression

Indexed keywords

BAYESIAN DECISION; DISTURBANCE; NATIVE VEGETATION CONDITION; POISSON-GAMMA REGRESSION; PRESSURE-STATE-RESPONSE; REGRESSION; DECISION THEORETIC APPROACH; GENERALIZED LINEAR MODEL; MONITORING ACTIVITIES; PRECAUTIONARY PRINCIPLE; SELECTION OF ATTRIBUTES; STATISTICAL MODELING; STATISTICAL MODELLING; VEGETATION MANAGEMENT;

BENCHMARKING; BIODIVERSITY; BIOMETRICS; DEBRIS; ECOSYSTEMS; FORESTRY; HEURISTIC METHODS; INDICATORS (INSTRUMENTS); MAXIMUM LIKELIHOOD; PLANT EXTRACTS; POISSON DISTRIBUTION; REGRESSION ANALYSIS; VEGETATION; COST FUNCTIONS; MIXTURES; STATISTICAL METHODS;

DECISION MAKING;

EID: 63849159641     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

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