Group norms and well-entrenched social norms will typically be followed by almost all members of a group or population, whereas greater deviance is usually accepted when norms are new, or when norms are not deemed to be socially important.
The validation of this model and the Markov chain Monte Carlo (MCMC) algorithm was carried out on simulated data from normally and t4 (i.e. a t-distribution with four degrees of freedom) distributed populations using the deviance information criterion (DIC) and a pseudo Bayes factor (PBF) measure to validate recently proposed model choice criteria.
Explanatory power went up even further using only US cases: 75% of deviance was explained with changes in population density, temperature, and precipitation.
Association between population mean and distribution of deviance in demographic surveys from 65 countries: cross sectional study.
First, we identified BRT parameters (learning rates, number of trees per step, tree complexity, bag fraction) that provided a reasonably quick reduction in deviance, in a model using only human population density as a predictor.
Emphasizes intensive reading of primary sources, which represent past attempts to count, calculate, measure, and model many dimensions of human social life (population, wealth, health, happiness, intelligence, crime, deviance, race).
We experimentally observed differences in susceptibility to both carbaryl and potassium dichromate among the studied pond populations of D. magna (analysis of deviance, P < 0.001).
Most populations (25 out of 30) showed deviance from HWE, when calculated over all loci.
Using all cases globally, a model composed of changes in population density and precipitation explained 58% of deviance [Table 4].
On the basis of the deviance information criterion, we inferred the most likely number of populations to be nine (Materials and Methods) and labeled each population by the most common geographic location and/or substrate from which the strains were originally isolated (Table S2).
