Clarify type of model variables expected by step methods

pymc3/step_methods/arraystep.py

@@ -134,7 +134,7 @@ class ArrayStep(BlockedStep):
     Parameters
     ----------
     vars: list
-        List of variables for sampler.
+        List of value variables for sampler.
     fs: list of logp Aesara functions
     allvars: Boolean (default False)
     blocked: Boolean (default True)
@@ -190,7 +190,7 @@ def __init__(self, vars, shared, blocked=True):
         """
         Parameters
         ----------
-        vars: list of sampling variables
+        vars: list of sampling value variables
         shared: dict of Aesara variable -> shared variable
         blocked: Boolean (default True)
         """
@@ -235,7 +235,7 @@ def __init__(self, vars, shared, blocked=True):
         """
         Parameters
         ----------
-        vars: list of sampling variables
+        vars: list of sampling value variables
         shared: dict of Aesara variable -> shared variable
         blocked: Boolean (default True)
         """

pymc3/step_methods/elliptical_slice.py

@@ -61,7 +61,7 @@ class EllipticalSlice(ArrayStep):
     Parameters
     ----------
     vars: list
-        List of variables for sampler.
+        List of value variables for sampler.
     prior_cov: array, optional
         Covariance matrix of the multivariate Gaussian prior.
     prior_chol: array, optional

pymc3/step_methods/hmc/base_hmc.py

@@ -66,7 +66,7 @@ def __init__(
 
         Parameters
         ----------
-        vars: list of Aesara variables
+        vars: list of value variables
         scaling: array_like, ndim = {1,2}
             Scaling for momentum distribution. 1d arrays interpreted matrix
             diagonal.

pymc3/step_methods/hmc/hmc.py

@@ -59,7 +59,7 @@ def __init__(self, vars=None, path_length=2.0, max_steps=1024, **kwargs):
 
         Parameters
         ----------
-        vars: list of Aesara variables
+        vars: list of value variables
         path_length: float, default=2
             total length to travel
         step_rand: function float -> float, default=unif

pymc3/step_methods/hmc/nuts.py

@@ -114,7 +114,7 @@ def __init__(self, vars=None, max_treedepth=10, early_max_treedepth=8, **kwargs)
 
         Parameters
         ----------
-        vars: list of Aesara variables, default all continuous vars
+        vars: list of value variables, default all continuous vars
         Emax: float, default 1000
             Maximum energy change allowed during leapfrog steps. Larger
             deviations will abort the integration.

pymc3/step_methods/metropolis.py

@@ -131,7 +131,7 @@ def __init__(
         Parameters
         ----------
         vars: list
-            List of variables for sampler
+            List of value variables for sampler
         S: standard deviation or covariance matrix
             Some measure of variance to parameterize proposal distribution
         proposal_dist: function
@@ -289,7 +289,7 @@ class BinaryMetropolis(ArrayStep):
     Parameters
     ----------
     vars: list
-        List of variables for sampler
+        List of value variables for sampler
     scaling: scalar or array
         Initial scale factor for proposal. Defaults to 1.
     tune: bool
@@ -389,7 +389,7 @@ class BinaryGibbsMetropolis(ArrayStep):
     Parameters
     ----------
     vars: list
-        List of variables for sampler
+        List of value variables for sampler
     order: list or 'random'
         List of integers indicating the Gibbs update order
         e.g., [0, 2, 1, ...]. Default is random

pymc3/step_methods/mlda.py

@@ -218,7 +218,7 @@ class MLDA(ArrayStepShared):
         Note this list excludes the model passed to the model
         argument above, which is the finest available.
     vars : list
-        List of variables for sampler
+        List of value variables for sampler
     base_sampler : string
         Sampler used in the base (coarsest) chain. Can be 'Metropolis' or
         'DEMetropolisZ'. Defaults to 'DEMetropolisZ'.

pymc3/step_methods/pgbart.py

@@ -34,7 +34,7 @@ class PGBART(ArrayStepShared):
     Parameters
     ----------
     vars: list
-        List of variables for sampler
+        List of value variables for sampler
     num_particles : int
         Number of particles for the conditional SMC sampler. Defaults to 10
     max_stages : int

pymc3/step_methods/sgmcmc.py

@@ -87,7 +87,7 @@ class BaseStochasticGradient(ArrayStepShared):
     Parameters
     ----------
     vars: list
-        List of variables for sampler
+        List of value variables for sampler
     batch_size`: int
         Batch Size for each step
     total_size: int

pymc3/step_methods/slicer.py

@@ -35,7 +35,7 @@ class Slice(ArrayStep):
     Parameters
     ----------
     vars: list
-        List of variables for sampler.
+        List of value variables for sampler.
     w: float
         Initial width of slice (Defaults to 1).
     tune: bool