What is Geometric approach to MCMC? | Basic Usage | Example 1: Sampling from a Multivariate Normal | Examining the Results | Visualizing the Results | Example 2: Bayesian Inference for Normal Data | Model Setup | Trace plots | Posterior Summaries | Visualizing the Posterior | Understanding the Output | Example 3: Univariate Mixture of Normals | Univariate Mixture of Normals: Run geomc with Default Settings | Univariate Mixture of Normals: Run geomc with Custom Random Walk Base Density | Univariate Mixture of Normals: Run geomc with Informed Approximate Targets | Univariate Mixture of Normals: Run geomc with Informed Approximate Targets along with a Custom Random Walk Base Density | Univariate Mixture of Normals: Run geomc with other choices of eps | Univariate Mixture of Normals: Run geomc with another (non-Gaussian) Informed Approximate Target | Univariate Mixture of Normals: Importance Sampling to estimate inner products for non-Gaussian densities | Example 4: Bivariate Mixture of Normals | Bivariate Mixture of Normals: Run geomc with Informed Approximate Targets along with a Custom Random Walk Base Density | Visualizing Bivariate Mixture Results | Bivariate Mixture of Normals: Run geomc with a diffuse $g$ | Bivariate Mixture of Normals: Comparison with Random Walk Metropolis | Example 2 Continued: Bayesian Inference for Normal Data with MALA Base Density | Example 5: Discrete Distribution (Binomial) | Discrete Distribution (Binomial): Run geomc with Random Walk Base (and user defined bhat.coef function) | Comparing with True Distribution | Discrete Distribution (Binomial): Run geomc with Reflecting Random Walk Base (and user defined bhat.coef function) | Discrete Distribution (Binomial): Run geomc with Uniform Base (an example with ind set as TRUE) | Summary of Non-Default Settings | When to Use Custom Specifications | References

Introduction | The Variable Selection Problem | Why Bayesian Variable Selection? | Basic Usage | Example 1: | Small Sparse Model | Data Generation | Run geomc.vs for Variable Selection | Examining the Results | Marginal Inclusion Probabilities (MIP) | Visualizing Marginal Inclusion Probabilities | The Median Probability Model | Posterior Mean of Coefficients | Model Space Exploration | Example 2: | Another Example to Illustrate Different Data Structures and Tuning Parameters | Generate Sparse Data | Visualize Sparse Matrix Structure | Memory Comparison | Generate Response Variable | Variable Selection using geomc.vs with Symmetric Random Walk Base | Understanding the Output | MCMC Samples | Log Posterior Values | Results Analysis | Marginal Inclusion Probabilities | Visualizing MIPs | Visualizing WMIPs | Model Selection Performance | Weighted Average Model | Coefficient Estimates | Key Takeaways | Variable Selection using geomc.vs with an Asymmetric Random Walk Base | Additional Parameters | Key Parameters | Summary | Appendix: The Model | Prior Specification | Posterior Computation | References