| Research History |
| Software Structure |
| Specie Sensitivity Distribution |
| BAYESIAN Inference |
| MCMC Simulation |
| DIC Optimization |
| Ecorisk & Uncertainty |
| Joint Probability Curve |
| Exergy SSD |
| Main Function Lists Panel |
| BMC-SSD Panel |
| Models Optimization Panel |
| JPC Panel |
| ExSSD Panel |
| Work Path & Output Results |
| Installation & Initialization |
| Folder & File Extraction |
| SSD Models & Ecorisk |
| JPC & Its Indicators |
| Models Optimization & Parameters |
| ExSSD Models & ExEcorisk |
Links
| College of Urban and Environment Science |
| Peking University |
BAYESIAN Inference
The Bayesian inference in the Bayesian theory is typically employed in deducing the posterior distribution of the models’ parameters. According to the Bayesian inference, the posterior distributions of the parameters are determined by their prior distributions and likelihood functions built by the observed data. The posterior distribution can be deduced as the following formula:
where | means conditional probability, H refers to the hypothetical event which may be effected by data observation, E refers the proven incident related to newly observed data, and it is not supporting incident of prior distribution P(H), P(H | E) is the posterior distribution—the occurrence probability of the original hypothetical event (H) by the support of new data (E), while P(E | H) is the occurrence probability of E when H happens— it is called likelihood function when they both occurs in same function. Furthermore, P(E) is called marginal likelihood function or simulation evidence, which represents the occurrence probability of a new event irrelevant to the hypothetical event.
