TITLE:
Bayesian Posterior Predictive Probability Happiness
AUTHORS:
Gabriela Rodríguez-Hernández, Galileo Domínguez-Zacarías, Carlos Juárez Lugo
KEYWORDS:
Bayesian Inference, Posterior Predictive Distribution, MCMC, Happiness
JOURNAL NAME:
Applied Mathematics,
Vol.7 No.8,
May
20,
2016
ABSTRACT: We propose to determine the underlying
causal structure of the elements of happiness from a set of empirically
obtained data based on Bayesian. We consider the proposal to study happiness as
a multidimensional construct which converges four dimensions with two different
Bayesian techniques, in the first we use the Bonferroni correction to estimate
the mean multiple comparisons, on this basis it is that we use the function t
and a z-test, in both cases the results do not vary, so it is decided to
present only those shown by the t test. In the Bayesian Multiple Linear
Regression, we prove that happiness can be explained through three dimensions.
The technical numerical used is MCMC, of four samples. The results show that
the sample has not atypical behavior too and that suitable modifications can be
described through a test. Another interesting result obtained is that the
predictive probability for the case of sense positive of life and personal
fulfillment dimensions exhibit a non-uniform variation.