Methoden vergleichen

	Bayesian Dosis-Wirkungs-Analyse	Bayes'sche Netzwerke	Logistische Regression
Fachgebiet	Epidemiologie	Bayes-Statistik	Forschungsstatistik
Familie	Process / pipeline	Bayesian methods	Process / pipeline
Entstehungsjahr	1990s–2000s (Bayesian formalization)	1988	1958
Urheber	Developed from classical frequentist dose-response traditions; Bayesian formulations advanced by Dempster, Gelman, and colleagues	Judea Pearl	David Roxbee Cox
Typ	Statistical modeling approach	Probabilistic graphical model	Method
Wegweisende Quelle	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	Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242.
Aliasnamen	Bayesian DRA, Bayesian dose-response modeling, Bayesian benchmark dose analysis, BDR	Bayes network, belief network, probabilistic graphical model, directed graphical model	logit model, binomial logistic regression, LR
Verwandt	3	4	3
Zusammenfassung	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.	Logistic regression is a statistical method for modeling the probability of a binary outcome (disease present/absent, success/failure) as a function of continuous and categorical predictors. Developed by David Roxbee Cox (1958), it solves the problem of predicting categorical outcomes by applying a logistic transformation to constrain predictions to the [0,1] probability interval, enabling accurate risk stratification, diagnostic prediction, and causal inference in epidemiology, medicine, and social science.