RJAGS & INLA Bayesian Data Science
Budget: £20 – £250 GBP
PROBLEM 1:
Create a JAGS model and explain how did you handle the fact that some of the observations are missing (NA) in the dataset (using Gaussian prior). Compute the Gelman-Rubin convergence diagnostics. Compute and print out the effective sample sizes (ESS) for each of the model parameters. Plots & discuss results. Update the model.
PROBLEM 2:
Create a dataframe that includes the mean speed of data. Fit the same model in INLA. Improve the model in b) by using more informative priors and more columns from the dataset. Perform model checks. Calculate the posterior probabilities
Create a JAGS model and explain how did you handle the fact that some of the observations are missing (NA) in the dataset (using Gaussian prior). Compute the Gelman-Rubin convergence diagnostics. Compute and print out the effective sample sizes (ESS) for each of the model parameters. Plots & discuss results. Update the model.
PROBLEM 2:
Create a dataframe that includes the mean speed of data. Fit the same model in INLA. Improve the model in b) by using more informative priors and more columns from the dataset. Perform model checks. Calculate the posterior probabilities