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In order to avoid market failures, innovation development requires prior constant interaction among the innovation process participants, i.e., their integration into innovation network.
We held the variance of the prior constant but changed its mean.
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However, holding the priors constant across the two experimental designs shows that the efficient design provides a lower D-error score.
Five demographic models were applied as coalescent priors: constant population size, exponential growth, expansion growth, logistic growth and Bayesian skyline plot (BSP) [39].
We then selected only Italian cattle and performed bayesian based population genetic inference and molecular dating under two coalescent priors: constant population size and the Bayesian skyline plot [ 58], with gene genealogies divided into three internode groups and effective population size function fitted with a piecewise constant function of population size change.
Bayesian analysis was then performed on the alignment in BEAST 1.6.1 [ 91], running 10,000,000 MCMC generations and logging every 1,000 generations, with the following departures from the default priors: constant population size; GTR+G substitution model with starting substitution matrix and gamma parameter values chosen using ModelTest.
Two independent MCMC runs were performed under an HKY+I+G substitution model, assuming a coalescent prior of constant population size [48].
We evaluated demographic history in the program BEAST v. 1. 5.4 [ 55] by comparing the likelihood of three different models for the coalescent tree prior - constant population size, expansion growth, and Bayesian skyline.
Uncorrelated lognormal relaxed molecular clocks with a coalescent approach prior assuming constant population size were used.
The population mean μ is assumed to have a prior p∝ constant.
A sensitivity analysis can therefore keep the prior expectation constant (e.g. 1, for other values please refer to the Supplementary Material) and change the prior variance.
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