手法を比較

	ベイジアン構造時系列モデル	ベイズ回帰	中断時系列分析（Interrupted Time Series, ITS）
分野	ベイズ	ベイズ	因果推論
系統	Bayesian methods	Bayesian methods	Regression model
提唱年	2014	—	2002
提唱者	Scott & Varian (2014); Brodersen et al. (2015)	—	Wagner, Soumerai, Zhang & Ross-Degnan (segmented regression); Bernal, Cummins & Gasparrini (tutorial)
種類	State-space model / Bayesian structural model	Bayesian linear model	Quasi-experimental segmented regression
原典	Scott, S. L. & Varian, H. R. (2014). Predicting the Present with Bayesian Structural Time Series. International Journal of Mathematical Modelling and Numerical Optimisation, 5(1/2), 4–23.	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	Bernal, J. L., Cummins, S., & Gasparrini, A. (2017). Interrupted time series regression for the evaluation of public health interventions: a tutorial. International Journal of Epidemiology, 46(1), 348-355.
別名	BSTS, Bayesian Yapısal Zaman Serisi (BSTS), bayesian state-space model, causal impact model	bayesian linear regression, probabilistic regression, bayesian regresyon	ITS analysis, segmented regression of time series, Kesintili Zaman Serisi (ITS) Analizi
関連	5	2	5
概要	Bayesian Structural Time Series (BSTS) is a state-space modelling framework, introduced by Scott and Varian (2014), that decomposes a time series into additive components — trend, seasonality, and regression — and estimates them jointly through Bayesian inference. It underpins Google's CausalImpact library and is a powerful tool for both forecasting and counterfactual causal analysis of interventions.	Bayesian regression is a probabilistic version of linear regression that treats the model parameters as uncertain quantities. Instead of returning a single best-fit estimate, it combines prior knowledge with the observed data to produce a full posterior probability distribution for each parameter, from which credible intervals and predictions are read off.	Interrupted Time Series analysis is a quasi-experimental design that estimates the effect of a single, well-dated intervention by comparing the trajectory of an outcome before and after it occurs. Formalised as segmented regression by Wagner and colleagues (2002) and popularised as a public-health evaluation tutorial by Bernal, Cummins and Gasparrini (2017), it separates the intervention's impact into a change in level and a change in slope.