Tiny style improvements

src/contexts.jl

@@ -188,7 +188,7 @@ getsampler(::IsParent, context::AbstractContext) = getsampler(childcontext(conte
 """
     struct DefaultContext <: AbstractContext end
 
-The `DefaultContext` is used by default to compute the log joint probability of the data 
+The `DefaultContext` is used by default to compute the log joint probability of the data
 and parameters when running the model.
 """
 struct DefaultContext <: AbstractContext end
@@ -199,7 +199,7 @@ NodeTrait(context::DefaultContext) = IsLeaf()
         vars::Tvars
     end
 
-The `PriorContext` enables the computation of the log prior of the parameters `vars` when 
+The `PriorContext` enables the computation of the log prior of the parameters `vars` when
 running the model.
 """
 struct PriorContext{Tvars} <: AbstractContext
@@ -213,8 +213,8 @@ NodeTrait(context::PriorContext) = IsLeaf()
         vars::Tvars
     end
 
-The `LikelihoodContext` enables the computation of the log likelihood of the parameters when 
-running the model. `vars` can be used to evaluate the log likelihood for specific values 
+The `LikelihoodContext` enables the computation of the log likelihood of the parameters when
+running the model. `vars` can be used to evaluate the log likelihood for specific values
 of the model's parameters. If `vars` is `nothing`, the parameter values inside the `VarInfo` will be used by default.
 """
 struct LikelihoodContext{Tvars} <: AbstractContext
@@ -229,10 +229,10 @@ NodeTrait(context::LikelihoodContext) = IsLeaf()
         loglike_scalar::T
     end
 
-The `MiniBatchContext` enables the computation of 
-`log(prior) + s * log(likelihood of a batch)` when running the model, where `s` is the 
-`loglike_scalar` field, typically equal to `the number of data points / batch size`. 
-This is useful in batch-based stochastic gradient descent algorithms to be optimizing 
+The `MiniBatchContext` enables the computation of
+`log(prior) + s * log(likelihood of a batch)` when running the model, where `s` is the
+`loglike_scalar` field, typically equal to `the number of data points / batch size`.
+This is useful in batch-based stochastic gradient descent algorithms to be optimizing
 `log(prior) + log(likelihood of all the data points)` in the expectation.
 """
 struct MiniBatchContext{Tctx,T} <: AbstractContext

src/model.jl

@@ -3,7 +3,7 @@
         f::F
         args::NamedTuple{argnames,Targs}
         defaults::NamedTuple{defaultnames,Tdefaults}
-        context::Ctx
+        context::Ctx=DefaultContext()
     end
 
 A `Model` struct with model evaluation function of type `F`, arguments of names `argnames`
@@ -1079,7 +1079,7 @@ end
 Return an array of log joint probabilities evaluated at each sample in an MCMC `chain`.
 
 # Examples
-	
+
 ```jldoctest
 julia> using MCMCChains, Distributions
 
@@ -1095,7 +1095,7 @@ julia> # construct a chain of samples using MCMCChains
        chain = Chains(rand(10, 2, 3), [:s, :m]);
 
 julia> logjoint(demo_model([1., 2.]), chain);
-```   
+```
 """
 function logjoint(model::Model, chain::AbstractMCMC.AbstractChains)
     var_info = VarInfo(model) # extract variables info from the model
@@ -1126,7 +1126,7 @@ end
 Return an array of log prior probabilities evaluated at each sample in an MCMC `chain`.
 
 # Examples
-	
+
 ```jldoctest
 julia> using MCMCChains, Distributions
 
@@ -1142,7 +1142,7 @@ julia> # construct a chain of samples using MCMCChains
        chain = Chains(rand(10, 2, 3), [:s, :m]);
 
 julia> logprior(demo_model([1., 2.]), chain);
-```   
+```
 """
 function logprior(model::Model, chain::AbstractMCMC.AbstractChains)
     var_info = VarInfo(model) # extract variables info from the model
@@ -1173,7 +1173,7 @@ end
 Return an array of log likelihoods evaluated at each sample in an MCMC `chain`.
 
 # Examples
-	
+
 ```jldoctest
 julia> using MCMCChains, Distributions
 
@@ -1189,7 +1189,7 @@ julia> # construct a chain of samples using MCMCChains
        chain = Chains(rand(10, 2, 3), [:s, :m]);
 
 julia> loglikelihood(demo_model([1., 2.]), chain);
-```  
+```
 """
 function Distributions.loglikelihood(model::Model, chain::AbstractMCMC.AbstractChains)
     var_info = VarInfo(model) # extract variables info from the model