방법 비교

	베이즈 용량-반응 분석	베이즈 네트워크
분야	역학	베이지안
계열	Process / pipeline	Bayesian methods
기원 연도	1990s–2000s (Bayesian formalization)	1988
창시자	Developed from classical frequentist dose-response traditions; Bayesian formulations advanced by Dempster, Gelman, and colleagues	Judea Pearl
유형	Statistical modeling approach	Probabilistic graphical model
원전	Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., & Rubin, D. B. (2013). Bayesian Data Analysis (3rd ed.). CRC Press. ISBN: 978-1439840955	Pearl, J. (1988). Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann. ISBN: 978-1558604797
별칭	Bayesian DRA, Bayesian dose-response modeling, Bayesian benchmark dose analysis, BDR	Bayes network, belief network, probabilistic graphical model, directed graphical model
관련	3	4
요약	Bayesian dose-response analysis models the relationship between the level of exposure (dose) to a substance and the magnitude or probability of a biological response, embedding that model in a Bayesian probabilistic framework. Unlike frequentist approaches that yield a single point estimate with confidence intervals, the Bayesian framework produces a full posterior distribution over model parameters, allowing explicit quantification of uncertainty, incorporation of prior scientific knowledge, and principled model averaging. It is widely applied in toxicology, pharmacology, environmental risk assessment, and clinical dose-finding studies.	A Bayesian network is a probabilistic graphical model, introduced by Judea Pearl in 1988, that encodes a set of variables and their conditional dependencies as a directed acyclic graph (DAG). Each node represents a variable; each directed edge encodes a direct probabilistic influence. By combining Bayes' rule with the graph's conditional independence structure, the model supports reasoning under uncertainty — computing the probability of any variable given observed evidence about others.