| Bayesian Spatial Point Process Classification Model | The BSPP model estimates the posterior distribution for reported foci across studies. |
| Emotional Signatures Across Networks and Regions of Interest | In addition, the matrix MC contains samples from the joint posterior distribution of regional intensity values that can be used for visualization and statistical inference. |
| Emotional Signatures Across Networks and Regions of Interest | As the elements of M are samples from the joint posterior distribution of intensity values, statistical inference on the difference 171d depends on its statistical distance from the origin, which is assessed by examining the proportion P of the samples that lie on the opposite side of the origin from 171,1, adjusting for the fact that the mean |
| The Bayesian Spatial Point Process (BSPP) Model | To develop a model for emotion categories and test its accuracy in diagnosing the emotions being cultivated in specific studies, we constructed a generative, Bayesian Spatial Point Process (BSPP) model of the joint posterior distribution of peak activation locations over the brain for each emotion category (see Methods and [38]). |
| The Bayesian Spatial Point Process (BSPP) Model | The MCMC procedure draws samples from the joint posterior distribution of the number and locations of peak activations in the brain given an emotion category. |
| The Bayesian Spatial Point Process (BSPP) Model | The posterior distribution is summarized in part by the in tensity function map representing the spatial posterior eXpected number of activation or population centers in each area across the brain given the emotion category; this can be used to interpret the activation pattern characteristic of an emotion category (Fig. |