The BNMA BN Repository

This Bayesian Network models the key drivers of price movements for six major cryptocurrencies, Bitcoin, Ethereum, Binance Coin, Ripple, Litecoin, and Tether, by integrating macro-financial indicators and social media factors. Built using a structured discretisation pipeline, it enables coin-specific causal analysis to uncover financial and behavioural influences.

This model is a variant of the "Assessing Environmental Injury - Main Model", here for reference sites only, showing the probability of environmental injury on a hypothetical endangered bird species (Pine Dipper) that is potentially affected by stressors from acid mine drainage (AMD) source. The stressors here are copper (Cu) and water pH affecting the Pine Dipper directly and indirectly through macroinvertebrate prey. This was built for the Natural Resource Damage Assessment and Restoration (NRDAR) program in the U.S., and appears as Supporting Information Figure S1 in Rowland et al. (in press).

This model is a proof-of-concept example of how Bayesian networks (BNs) may be helpful in assessing natural resource injury within the Natural Resource Damage Assessment and Restoration (NRDAR) program in the U.S.

The data set for this model can be found at <abnms.org...>

This BN is an example of dynamic Bayesian network which includes feedback loops. The feedback loop represents the commonly accepted feedback relationship between socioeconomic status and education.

An assessment of the expected value of putting in a fence to promote plant survival, in the face of factors that affect the durability of the fence.

The Decision Graph is applied for the assessment and optimization of an existing threshold-based debris flow warning system. To model the warning system and compute the technical and inherent reliability, the Bayesian Network, which is the Decision Graph without the utility node, can be applied alone. Paper: <www.era.bgu.tum.de...>.