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.
Highlights
Bayesian networks emerged about three decades ago as alternatives to conventional systems-oriented decisionmaking and forecasting under uncertainty in probabilistic terms [1]
The aim of this study was to determine the underlying causal structure of the elements of happiness from a set of empirically obtained data. It is considered a Bayesian statistical parameter, which is inferred as an uncertain event, in this study the knowledge about happiness is not accurate and is subject to uncertainty, happiness can be described by a probability distribution
The focus recent developments of Markov chain Monte Carlo algorithms, in many situations the only way to integrate over the parameter space
Summary
Bayesian networks emerged about three decades ago as alternatives to conventional systems-oriented decisionmaking and forecasting under uncertainty in probabilistic terms [1]. The aim of this study was to determine the underlying causal structure of the elements of happiness from a set of empirically obtained data It is considered a Bayesian statistical parameter, which is inferred as an uncertain event, in this study the knowledge about happiness is not accurate and is subject to uncertainty, happiness can be described by a probability distribution. Scholars have noticed that happiness is not a single thing, but it can be broken down into its constituent elements Considering this information, Alarcón [28] proposes to study happiness as a multidimensional construct which converges satisfaction of what has been achieved, positive attitudes toward life, (experiences that reflect positive feelings concerning one’s self and life) personal fulfillment, and joy of living. The factorial analysis of principal components and varimax rotation, revealed that happiness is a multidimensional behavior, consisting of four dimension: X1 Sense positive of life, X 2 Satisfaction with life, X3 Personal fulfillment and X 4 Joy of living
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