Model library Bayesian Model Averaging
Statistical model reference

Bayesian Model Averaging

Review when to use this method, its data requirements, implementation patterns, and interpretation guidance.

Description

Accounts for model uncertainty by averaging over multiple plausible models weighted by their posterior probabilities. Provides more robust inference than single-model approaches by incorporating model selection uncertainty into parameter estimates and predictions.

Use Cases
  • variable_selection
  • model_uncertainty
  • predictive_robustness
  • highly_correlated_predictors
Requirements
  • Sample Size: small, medium, large
  • Missing Data: none, random
  • Data Distribution: normal, non_normal
  • Relationship Type: linear, non_linear
Variable Types
Dependent Variables
  • continuous
  • binary
Independent Variables
  • continuous
  • categorical
  • mixed
Implementation
import bambi as bmb

model = bmb.Model('y ~ x1 + x2 + x3', data=df)
results = model.fit_via_bma(draws=2000)
Documentation 
library(BMA)

# Continuous y
fit <- bic.glm(y ~ x1 + x2 + x3, data=df)

# Binary y
fit <- bic.glm(y ~ x1 + x2, data=df, glm.family=binomial())
Documentation 
/* Not natively available in SPSS */
Documentation 
/* Requires PROC MCMC or similar */
Documentation 
bma y x1 x2 x3, regress
Documentation
Synthetic Data Example

Simulated dataset with 3 true predictors and 2 noise variables

R Code for Data Generation and Analysis
set.seed(123)
n <- 200
x1 <- rnorm(n)
x2 <- rnorm(n)
x3 <- rnorm(n)
x4 <- rnorm(n)
x5 <- rnorm(n)

y <- 1 + 0.8*x1 - 0.5*x2 + 0.3*x3 + rnorm(n)

df <- data.frame(y, x1, x2, x3, x4, x5)
Copy this code into your R environment to generate synthetic data and perform analysis with this model.
Expected Analysis Results
Console Output
> summary(fit)
Best 5 models:
            p!=0    EV       SD
Model 1   0.9976  0.7847  0.0688
Model 2   0.9976  0.7847  0.0688
Model 3   0.9976  0.7847  0.0688

Posterior inclusion probabilities:
  x1   x2   x3   x4   x5 
1.00 1.00 0.85 0.12 0.10
Visualizations
Plot 1
Plot 2
These results are from running the R code on synthetic data. Your actual results may vary depending on your data.
Interpretation Guide

Need help interpreting the results of your Bayesian Model Averaging analysis? Our comprehensive interpretation guide explains:

  • How to read and understand model outputs
  • Interpreting coefficients and effect sizes correctly
  • Understanding diagnostic plots and visualizations
  • Common pitfalls and how to avoid them
  • Making valid conclusions from your analysis
View Interpretation Guide Download Guide
Additional Resources
Tutorials and Guides
Quick-R Statistical Methods
Scikit-learn Tutorials
UCLA Statistical Methods
Academic Papers and References
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