.png){kind=icon}
.png){kind=icon}
Return
Approximate Bayesian Computation and Simulation-Based Inference for Complex Stochastic Epidemic Models
.png){kind=icon}
OA
AI
DOI：10.1214/17-STS618.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Approximate Bayesian Computation (ABC) and other simulation-based inference methods are becoming increasingly used for inference in complex systems, due to their relative ease-of-implementation. We briefly review some of the more popular variants of ABC and their application in epidemiology, before using a real-world model of HIV transmission to illustrate some of challenges when applying ABC methods to high-dimensional, computationally intensive models. We then discuss an alternative approach-history matching-that aims to address some of these issues, and conclude with a comparison between these different methodologies.
Keywords:
Approximate Bayesian Computation
history matching
emulation
Bayesian inference
infectious disease models
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
3.4
Papers:
1.0K
Citations:
8.7K
Organization
View more organizations