A priori probability
A01=Marc Mangel
A01=Ray Hilborn
Alternative hypothesis
Author_Marc Mangel
Author_Ray Hilborn
Bayes' theorem
Bayesian
Bayesian inference
Bayesian information criterion
Bayesian statistics
Big O notation
Binomial distribution
Bootstrapping (statistics)
Calculation
Category=PBW
Category=PDN
Category=PSAF
Category=PST
Category=PSV
Category=UGK
Central limit theorem
Chi-squared test
Complexity
Confidence interval
Confidence region
Conjugate prior
Curve fitting
Detection
Ecology
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eq_computing
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
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Equation
Error
Estimation
Expected value
Floor and ceiling functions
Free parameter
Generalized linear model
Golden section search
Goodness of fit
Inverse probability
Leslie matrix
Likelihood function
Likelihood-ratio test
Logistic map
Logistic regression
Maximum likelihood estimation
Meta-analysis
Model selection
Monte Carlo method
Multinomial distribution
Negative binomial distribution
Newton's method
Normal distribution
Outlier
Overdispersion
P-value
Parameter
Poisson distribution
Poisson point process
Posterior probability
Prediction
Prior probability
Probability
Probability distribution
Probability theory
Pseudocode
Random search
Random variable
Resampling (statistics)
Residual sum of squares
Result
Sampling (statistics)
Search theory
Skewness
Special case
Standard deviation
Stochastic
Sum of squares
Uncertainty
Variance
Product details
- ISBN 9780691034973
- Weight: 369g
- Dimensions: 140 x 216mm
- Publication Date: 06 Mar 1997
- Publisher: Princeton University Press
- Publication City/Country: US
- Product Form: Paperback
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The modern ecologist usually works in both the field and laboratory, uses statistics and computers, and often works with ecological concepts that are model-based, if not model-driven. How do we make the field and laboratory coherent? How do we link models and data? How do we use statistics to help experimentation? How do we integrate modeling and statistics? How do we confront multiple hypotheses with data and assign degrees of belief to different hypotheses? How do we deal with time series (in which data are linked from one measurement to the next) or put multiple sources of data into one inferential framework? These are the kinds of questions asked and answered by The Ecological Detective. Ray Hilborn and Marc Mangel investigate ecological data much as a detective would investigate a crime scene by trying different hypotheses until a coherent picture emerges. The book is not a set of pat statistical procedures but rather an approach. The Ecological Detective makes liberal use of computer programming for the generation of hypotheses, exploration of data, and the comparison of different models. The authors' attitude is one of exploration, both statistical and graphical.
The background required is minimal, so that students with an undergraduate course in statistics and ecology can profitably add this work to their tool-kit for solving ecological problems.
Ray Hilborn is Professor in the School of Fisheries, University of Washington and the coauthor, with Carl Walters, of Quantitative Fisheries Stock Assessment. Marc Mangel is Professor of Environmental Studies and a Fellow at College Eight at the University of California, Santa Cruz, and a Visiting Professor at the University of Glasgow. He is the author of Decision and Control in Uncertain Resource Systems and coauthor, with Colin Clark, of Dynamic Modeling in Behavioral Ecology.
