Salīdzināt metodes

	Telpiskā beijesisko modelēšanas vidējo vērtību noteikšana	Beijesiskā regresija
Nozare	Bajesa metodes	Bajesa metodes
Saime	Bayesian methods	Bayesian methods
Izcelsmes gads	2008	—
Autors	LeSage & Fischer (building on Raftery et al. BMA framework, 1997)	—
Tips	Bayesian model combination with spatial structure	Bayesian linear model
Pirmavots	LeSage, J. P. & Pace, R. K. (2009). Introduction to Spatial Econometrics. CRC Press / Taylor & Francis. ISBN: 978-1420064247	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
Citi nosaukumi	spatial BMA, BMA for spatial data, Bayesian model averaging with spatial effects, spatial model uncertainty averaging	bayesian linear regression, probabilistic regression, bayesian regresyon
Saistītās	5	2
Kopsavilkums	Spatial Bayesian model averaging (spatial BMA) extends classical BMA to settings where observations are georeferenced and spatial dependence must be modelled. Rather than selecting a single spatial regression model — which spatial weight matrix to use, which regressors to include, which spatial lag or error structure to adopt — it averages the predictions and parameter estimates across all candidate models, weighting each by its posterior probability given the data.	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.