export predict

Author: sunxd3

docs/src/api.md

@@ -104,6 +104,32 @@ Similarly, we can [`unfix`](@ref) variables, i.e. return them to their original
 unfix
 ```
 
+## Predicting
+
+DynamicPPL provides functionality for generating samples from the posterior predictive distribution through the `predict` function. This allows you to use posterior parameter samples to generate predictions for unobserved data points.
+
+```@docs
+predict
+```
+
+The `predict` function has two main methods:
+
+1. For `AbstractVector{<:AbstractVarInfo}` - useful when you have a collection of `VarInfo` objects representing posterior samples.
+2. For `MCMCChains.Chains` - useful when you have posterior samples in the form of a `Chains` object from MCMCChains.jl.
+
+### Basic Usage
+
+The typical workflow for posterior prediction involves:
+
+1. Fitting a model to observed data to obtain posterior samples
+2. Creating a new model instance with some variables marked as missing (unobserved)
+3. Using `predict` to generate samples for these missing variables based on the posterior parameter samples
+
+When using `predict` with `MCMCChains.Chains`, you can control which variables are included in the output with the `include_all` parameter:
+
+- `include_all=false` (default): Include only newly predicted variables
+- `include_all=true`: Include both parameters from the original chain and predicted variables
+
 ## Models within models
 
 One can include models and call another model inside the model function with `left ~ to_submodel(model)`.

src/DynamicPPL.jl

@@ -121,6 +121,7 @@ export AbstractVarInfo,
     decondition,
     fix,
     unfix,
+    predict,
     prefix,
     returned,
     to_submodel,