humanitarian {bnRep} | R Documentation |
humanitarian Bayesian Network
Description
You only derive once (YODO): Automatic differentiation for efficient sensitivity analysis in Bayesian networks.
Format
A discrete Bayesian network to assess the country-level risk associated with humanitarian crises and disasters. The Bayesian network is learned as in the referenced paper. The vertices are:
- RISK
(low, medium, high);
- EARTHQUAKE
(low, medium, high);
- FLOOD
(low, medium, high);
- TSUNAMI
(low, medium, high);
- TROPICAL_CYCLONE
(low, medium, high);
- DROUGHT
(low, medium, high);
- EPIDEMIC
(low, medium, high);
- PCR
Projected conflict risk (low, medium, high);
- CHVCI
Current highly violent conflict intensity (low, medium, high);
- D_AND_D
Development and deprivation (low, medium, high);
- ECON_DEP
Economic dependency (low, medium, high);
- UNP_PEOPLE
Unprotected people (low, medium, high);
- HEALTH_COND
Health conditions (low, medium, high);
- CHILDREN_U5
(low, medium, high);
- RECENT_SHOCKS
(low, medium, high);
- FOOD_SECURITY
(low, medium, high);
- OTHER_VULN_GROUPS
Other vulnerable groups (low, medium, high);
- GOVERNANCE
(low, medium, high);
- COMMUNICATION
(low, medium, high);
- PHYS_INFRA
Physical infrastructure (low, medium, high);
- ACCESS_TO_HEALTH
(low, medium, high);
Value
An object of class bn.fit
. Refer to the documentation of bnlearn
for details.
References
Ballester-Ripoll, R., & Leonelli, M. (2022, September). You only derive once (YODO): automatic differentiation for efficient sensitivity analysis in Bayesian networks. In International Conference on Probabilistic Graphical Models (pp. 169-180). PMLR.