Longer data plans are expected to encourage

marginal data users in subscribing for data.

whereby the denominator is the

marginal data density, which can serve as a measure of overall model fit.

Both average data and aggregated

marginal data using average plant generation efficiency may provide a good indication of expected impact of investment decisions for planning and screening purposes.

Given the likelihood and the prior density of model parameters, one can simulate the posterior distribution and compute the

marginal data density (MDD).

The term p([Y.sup.T]|[H.sub.i]) is called (Bayesian)

marginal data density and defined as:

In a Bayesian framework such a measure is provided by the so-called

marginal data density, which arises naturally in the computation of posterior model odds.

Throughout history, scientists have successfully defended

marginal data, and theories that sounded silly have proved revolutionary.

The other, co-authored by Jennifer Means, makes the laudable point that the media's attention to

marginal data often obscures more interesting (and newsworthy) relationships between variables.

Marginal plant- operate to Should emissison offsets be

marginal data meet changes in demand calculated with marginal bringing about continuous data and carbon footprints variations in the calculated with average emissions.

This is confirmed by a comparison of the

marginal data densities (MDD).

Table 1: Log

Marginal Data Densities and Posterior Odds Specification ln p(Y|[lambda]) Posterior Odds DSGE Model -321.16 1.000 DSGE-VAR, [lambda] = 5.0 -313.58 1967 DSGE-VAR, [lambda] = 1.0 -297.59 1.7E9 DSGE-VAR, [lambda] = 0.5 -289.75 4E13 Notes: The

marginal data densities are obtained by integrating the Likelihood function with respect to the model parameters, weighted by the prior density conditional on [lambda].

According to Bayes Theorem, the posterior probabilities for the hyperparameter are proportional to the

marginal data density