Quick start | Did it work? | How greta uses Python | When the default isn't right | Using greta offline | Prime the managed (uv) environment while online | Pin dependency versions | I already have Python or TensorFlow installed | I need specific dependency versions | What you can actually choose | TensorFlow 2.16 and 2.17 cannot be used | Building an unsupported installation on purpose | I want the conda workflow | How install_greta_deps() works | Advanced: how greta chooses a Python environment | Starting over | Troubleshooting

How do I install greta dependencies? | How do I know if greta has the right versions of Python dependencies installed? | Is there an alternative way to install Python dependencies? | I get the message: "Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX AVX2" | How do I make my results reproducible?

Installation | Installing TensorFlow | DiagrammeR | How greta works | Building a model | Data | explicit vs. automatic conversion | data structures | Variables and priors | variables without probability distributions | variables with probability distributions | variables with truncated distributions | Operations | Extract and replace | Functions | Likelihood | Defining the model | Plotting | Sampling | Tweaking the sampler

Declaring data mutable | Fitting the same model to different data | The dimensions must be the same as the original data | data_values() must be used with as_data_mutable() | Other applications | References

Common models | Linear regression | Multiple linear regression | Multiple Poisson regression | Multiple categorical regression | Multiple linear regression with LASSO prior | Hierarchical linear regression | Random intercept-slope model | Random intercept-slope model (with correlated effects) | Common Bayesian priors | Improper flat prior | Ridge prior | Exponential-normal prior | Horseshoe prior | Regularised horseshoe prior | Advanced Bayesian models | Hierarchical linear regression in general conditional formulation | Hierarchical linear regression in general marginal formulation | Bayesian neural network | Factor analysis | BUGS models | Air | data | greta code | BUGS/JAGS code | Stan code | Beetles | Stan models | Lightspeed | Eight schools | Ecological models | Logistic regression | Poisson regression | Logistic regression with error term | Multiple species modelling independently and concurrently | Multiple species with partial pooling of regression coefficients | Multiple species with sub-model for regression coefficients | Cormack-Jolly-Seber model

Gaussian processes in greta | Example