| 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 |
MCMC Simulation
Markov Chain describes a state sequence where each state depended on the previous finite states, and it is a sequence formed by random variables with Markov property. Therefore, Markov Chain is regarded as a contingent probability event, which means the conditional probability distribution of event Xn+1 for past states is a function of Xn, which can be conducted as follows:
This formula is very much alike the Bayesian inference. Markov Chain, combined with Monte Carlo simulation based on prior distribution (known as MCMC simulation), can successfully deduce the posterior distributions of the parameters in Bayesian inference, which enables our users to obtain the parameter distributions of a certain model in our software.
