diff --git a/.github/workflows/CI.yml b/.github/workflows/CI.yml
index 22e67ad..acf0276 100644
--- a/.github/workflows/CI.yml
+++ b/.github/workflows/CI.yml
@@ -20,7 +20,6 @@ jobs:
       fail-fast: false
       matrix:
         version:
-          - '1.9'
           - '1.10'
           - '1'
         os:
diff --git a/CHANGELOG.md b/CHANGELOG.md
index 0958616..a0b9851 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -6,6 +6,18 @@ The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.1.0/),
 
 *Note*: We try to adhere to these practices as of version [v0.2.1].
 
+
+## Version [1.2.0] - 2024-12-03
+
+### Changed
+
+- Largely removed unicode characters from code base. [#134]
+- Removed legacy v1.9 from CI testing. [#134]
+
+### Added
+
+- Added general support for MLJ [#126] [#134]
+
 ## Version [1.1.1] - 2024-09-12
 
 ### Changed
diff --git a/Project.toml b/Project.toml
index 0124164..609cd6a 100644
--- a/Project.toml
+++ b/Project.toml
@@ -1,7 +1,7 @@
 name = "LaplaceRedux"
 uuid = "c52c1a26-f7c5-402b-80be-ba1e638ad478"
 authors = ["Patrick Altmeyer"]
-version = "1.1.1"
+version = "1.2.0"
 
 [deps]
 CategoricalDistributions = "af321ab8-2d2e-40a6-b165-3d674595d28e"
@@ -32,7 +32,7 @@ MLJBase = "1"
 MLJModelInterface = "1.8.0"
 MLUtils = "0.4"
 Optimisers = "0.2, 0.3"
-Random = "1.9, 1.10"
+Random = "1"
 Statistics = "1"
 Tables = "1.10.1"
 Test = "1"
diff --git a/docs/src/tutorials/regression.qmd b/docs/src/tutorials/regression.qmd
index 2faee5a..5059649 100644
--- a/docs/src/tutorials/regression.qmd
+++ b/docs/src/tutorials/regression.qmd
@@ -128,7 +128,7 @@ then we can plot the calibration plot of our neural model
 
 ```{julia}
 #| output: true
-Calibration_Plot(la,y_test,vec(predicted_distributions);n_bins = 20)
+calibration_plot(la,y_test,vec(predicted_distributions);n_bins = 20)
 ``` 
 
 and compute the sharpness of the predictive distribution
diff --git a/src/baselaplace/core_struct.jl b/src/baselaplace/core_struct.jl
index 41599bd..a6cfd0d 100644
--- a/src/baselaplace/core_struct.jl
+++ b/src/baselaplace/core_struct.jl
@@ -26,10 +26,14 @@ Container for the parameters of a Laplace approximation.
 - `hessian_structure::HessianStructure`: the structure of the Hessian. Possible values are `:full` and `:kron` or a concrete subtype of `HessianStructure`.
 - `backend::Symbol`: the backend to use. Possible values are `:GGN` and `:Fisher`.
 - `curvature::Union{Curvature.CurvatureInterface,Nothing}`: the curvature interface. Possible values are `nothing` or a concrete subtype of `CurvatureInterface`.
-- `σ::Real`: the observation noise
-- `μ₀::Real`: the prior mean
-- `λ::Real`: the prior precision
-- `P₀::Union{Nothing,AbstractMatrix,UniformScaling}`: the prior precision matrix
+- `observational_noise::Real`: the observation noise
+- `σ::Real`: alias for `observational_noise`.
+- `prior_mean::Real`: the prior mean of the network parameters.
+- `μ₀::Real`: alias for `prior_mean`.
+- `prio_precision::Real`: the prior precision for the network parameters.
+- `λ::Real`: alias for `prior_precision`.
+- `prior_precision_matrix::Union{Nothing,AbstractMatrix,UniformScaling}`: the prior precision matrix for the network parameters.
+- `P₀::Union{Nothing,AbstractMatrix,UniformScaling}`: alias for `prior_precision_matrix`.
 """
 Base.@kwdef struct LaplaceParams
     subset_of_weights::Symbol = :all
@@ -37,10 +41,30 @@ Base.@kwdef struct LaplaceParams
     hessian_structure::Union{HessianStructure,Symbol,String} = FullHessian()
     backend::Symbol = :GGN
     curvature::Union{Curvature.CurvatureInterface,Nothing} = nothing
-    σ::Real = 1.0
-    μ₀::Real = 0.0
-    λ::Real = 1.0
-    P₀::Union{Nothing,AbstractMatrix,UniformScaling} = nothing
+    observational_noise::Real = 1.0
+    σ::Real = observational_noise
+    prior_mean::Real = 0.0
+    μ₀::Real = prior_mean
+    prior_precision::Real = 1.0
+    λ::Real = prior_precision
+    prior_precision_matrix::Union{Nothing,AbstractMatrix,UniformScaling} = nothing
+    P₀::Union{Nothing,AbstractMatrix,UniformScaling} = prior_precision_matrix
+end
+
+function Base.getproperty(ce::LaplaceParams, sym::Symbol)
+    sym = sym === :σ ? :observational_noise : sym
+    sym = sym === :μ₀ ? :prior_mean : sym
+    sym = sym === :λ ? :prior_precision : sym
+    sym = sym === :P₀ ? :prior_precision_matrix : sym
+    return Base.getfield(ce, sym)
+end
+
+function Base.setproperty!(ce::LaplaceParams, sym::Symbol, val)
+    sym = sym === :σ ? :observational_noise : sym
+    sym = sym === :μ₀ ? :prior_mean : sym
+    sym = sym === :λ ? :prior_precision : sym
+    sym = sym === :P₀ ? :prior_precision_matrix : sym
+    return Base.setfield!(ce, sym, val)
 end
 
 include("estimation_params.jl")
@@ -96,7 +120,7 @@ la = Laplace(nn, likelihood=:regression)
 """
 function Laplace(model::Any; likelihood::Symbol, kwargs...)
     args = LaplaceParams(; kwargs...)
-    @assert !(args.σ != 1.0 && likelihood != :regression) "Observation noise σ ≠ 1 only available for regression."
+    @assert !(args.observational_noise != 1.0 && likelihood != :regression) "Observation noise σ ≠ 1 only available for regression."
 
     # Unpack arguments and wrap in containers:
     est_args = EstimationParams(args, model, likelihood)
diff --git a/src/baselaplace/optimize_prior.jl b/src/baselaplace/optimize_prior.jl
index 08f3c49..dd253a7 100644
--- a/src/baselaplace/optimize_prior.jl
+++ b/src/baselaplace/optimize_prior.jl
@@ -19,8 +19,8 @@ function optimize_prior!(
 )
 
     # Setup:
-    logP₀ = isnothing(λinit) ? log.(unique(diag(la.prior.P₀))) : log.([λinit])   # prior precision (scalar)
-    logσ = isnothing(σinit) ? log.([la.prior.σ]) : log.([σinit])                 # noise (scalar)
+    logP₀ = isnothing(λinit) ? log.(unique(diag(la.prior.prior_precision_matrix))) : log.([λinit])   # prior precision (scalar)
+    logσ = isnothing(σinit) ? log.([la.prior.observational_noise]) : log.([σinit])                 # noise (scalar)
     opt = Adam(lr)
     show_every = round(n_steps / 10)
     i = 0
diff --git a/src/baselaplace/posterior.jl b/src/baselaplace/posterior.jl
index ea0e4c5..c8d1dde 100644
--- a/src/baselaplace/posterior.jl
+++ b/src/baselaplace/posterior.jl
@@ -5,26 +5,38 @@ Container for the results of a Laplace approximation.
 
 # Fields
 
-- `μ::AbstractVector`: the MAP estimate of the parameters
+- `posterior_mean::AbstractVector`: the MAP estimate of the parameters
 - `H::Union{AbstractArray,AbstractDecomposition,Nothing}`: the Hessian matrix
 - `P::Union{AbstractArray,AbstractDecomposition,Nothing}`: the posterior precision matrix
-- `Σ::Union{AbstractArray,Nothing}`: the posterior covariance matrix
+- `posterior_covariance_matrix::Union{AbstractArray,Nothing}`: the posterior covariance matrix
 - `n_data::Union{Int,Nothing}`: the number of data points
 - `n_params::Union{Int,Nothing}`: the number of parameters
 - `n_out::Union{Int,Nothing}`: the number of outputs
 - `loss::Real`: the loss value
 """
 mutable struct Posterior
-    μ::AbstractVector
+    posterior_mean::AbstractVector
     H::Union{AbstractArray,AbstractDecomposition,Nothing}
     P::Union{AbstractArray,AbstractDecomposition,Nothing}
-    Σ::Union{AbstractArray,Nothing}
+    posterior_covariance_matrix::Union{AbstractArray,Nothing}
     n_data::Union{Int,Nothing}
     n_params::Union{Int,Nothing}
     n_out::Union{Int,Nothing}
     loss::Real
 end
 
+function Base.getproperty(ce::Posterior, sym::Symbol)
+    sym = sym === :μ ? :posterior_mean : sym
+    sym = sym === :Σ ? :posterior_covariance_matrix : sym
+    return Base.getfield(ce, sym)
+end
+
+function Base.setproperty!(ce::Posterior, sym::Symbol, val)
+    sym = sym === :μ ? :posterior_mean : sym
+    sym = sym === :Σ ? :posterior_covariance_matrix : sym
+    return Base.setfield!(ce, sym, val)
+end
+
 """
     Posterior(model::Any, est_params::EstimationParams)
 
diff --git a/src/baselaplace/predicting.jl b/src/baselaplace/predicting.jl
index feb457b..62e82fd 100644
--- a/src/baselaplace/predicting.jl
+++ b/src/baselaplace/predicting.jl
@@ -135,7 +135,7 @@ function predict(
     if la.likelihood == :regression
 
         # Add observational noise:
-        pred_var = fvar .+ la.prior.σ^2
+        pred_var = fvar .+ la.prior.observational_noise^2
         fstd = sqrt.(pred_var)
         pred_dist = [Normal(fμ[i], fstd[i]) for i in axes(fμ, 2)]
 
diff --git a/src/baselaplace/prior.jl b/src/baselaplace/prior.jl
index accec86..34e0eb5 100644
--- a/src/baselaplace/prior.jl
+++ b/src/baselaplace/prior.jl
@@ -5,16 +5,32 @@ Container for the prior parameters of a Laplace approximation.
 
 # Fields
 
-- `σ::Real`: the observation noise
-- `μ₀::Real`: the prior mean
-- `λ::Real`: the prior precision
-- `P₀::Union{Nothing,AbstractMatrix,UniformScaling}`: the prior precision matrix
+- `observational_noise::Real`: the observation noise
+- `prior_mean::Real`: the prior mean
+- `prior_precision::Real`: the prior precision
+- `prior_precision_matrix::Union{Nothing,AbstractMatrix,UniformScaling}`: the prior precision matrix
 """
 mutable struct Prior
-    σ::Real
-    μ₀::Real
-    λ::Real
-    P₀::Union{Nothing,AbstractMatrix,UniformScaling}
+    observational_noise::Real
+    prior_mean::Real
+    prior_precision::Real
+    prior_precision_matrix::Union{Nothing,AbstractMatrix,UniformScaling}
+end
+
+function Base.getproperty(ce::Prior, sym::Symbol)
+    sym = sym === :σ ? :observational_noise : sym
+    sym = sym === :μ₀ ? :prior_mean : sym
+    sym = sym === :λ ? :prior_precision : sym
+    sym = sym === :P₀ ? :prior_precision_matrix : sym
+    return Base.getfield(ce, sym)
+end
+
+function Base.setproperty!(ce::Prior, sym::Symbol, val)
+    sym = sym === :σ ? :observational_noise : sym
+    sym = sym === :μ₀ ? :prior_mean : sym
+    sym = sym === :λ ? :prior_precision : sym
+    sym = sym === :P₀ ? :prior_precision_matrix : sym
+    return Base.setfield!(ce, sym, val)
 end
 
 """
@@ -23,16 +39,16 @@ end
 Extracts the prior parameters from a `LaplaceParams` object.
 """
 function Prior(params::LaplaceParams, model::Any, likelihood::Symbol)
-    P₀ = params.P₀
+    prior_precision_matrix = params.prior_precision_matrix
     n = LaplaceRedux.n_params(model, EstimationParams(params, model, likelihood))
-    if typeof(P₀) <: UniformScaling
-        P₀ = P₀(n)
-    elseif isnothing(P₀)
-        P₀ = UniformScaling(params.λ)(n)
+    if typeof(prior_precision_matrix) <: UniformScaling
+        prior_precision_matrix = prior_precision_matrix(n)
+    elseif isnothing(prior_precision_matrix)
+        prior_precision_matrix = UniformScaling(params.prior_precision)(n)
     end
     # Sanity:
-    if isa(P₀, AbstractMatrix)
-        @assert all(size(P₀) .== n) "Dimensions of prior Hessian $(size(P₀)) do not align with number of parameters ($n)"
+    if isa(prior_precision_matrix, AbstractMatrix)
+        @assert all(size(prior_precision_matrix) .== n) "Dimensions of prior Hessian $(size(prior_precision_matrix)) do not align with number of parameters ($n)"
     end
-    return Prior(params.σ, params.μ₀, params.λ, P₀)
+    return Prior(params.observational_noise, params.prior_mean, params.prior_precision, prior_precision_matrix)
 end
diff --git a/src/baselaplace/utils.jl b/src/baselaplace/utils.jl
index 853a3e0..e473c3f 100644
--- a/src/baselaplace/utils.jl
+++ b/src/baselaplace/utils.jl
@@ -18,7 +18,7 @@ LaplaceRedux.n_params(la::Laplace) = LaplaceRedux.n_params(la.model, la.est_para
 Helper function to extract the prior mean of the parameters from a Laplace approximation.
 """
 function get_prior_mean(la::Laplace)
-    return la.prior.μ₀
+    return la.prior.prior_mean
 end
 
 """
@@ -27,7 +27,7 @@ end
 Helper function to extract the prior precision matrix from a Laplace approximation.
 """
 function prior_precision(la::Laplace)
-    return la.prior.P₀
+    return la.prior.prior_precision_matrix
 end
 
 """
@@ -39,7 +39,7 @@ on the last layer of the NN, of a `Flux.Chain` with Laplace approximation.
 outdim(la::AbstractLaplace) = outdim(la.model)
 
 @doc raw"""
-    posterior_precision(la::AbstractLaplace, H=la.posterior.H, P₀=la.prior.P₀)
+    posterior_precision(la::AbstractLaplace, H=la.posterior.H, P₀=la.prior.prior_precision_matrix)
 
 Computes the posterior precision ``P`` for a fitted Laplace Approximation as follows,
 
@@ -47,7 +47,7 @@ Computes the posterior precision ``P`` for a fitted Laplace Approximation as fol
 
 where ``\sum_{n=1}^N\nabla_{\theta}^2\log p(\mathcal{D}_n|\theta)|_{\hat\theta}=H`` is the Hessian and ``\nabla_{\theta}^2 \log p(\theta)|_{\hat\theta}=P_0`` is the prior precision and ``\hat\theta`` is the MAP estimate.
 """
-function posterior_precision(la::AbstractLaplace, H=la.posterior.H, P₀=la.prior.P₀)
+function posterior_precision(la::AbstractLaplace, H=la.posterior.H, P₀=la.prior.prior_precision_matrix)
     @assert !isnothing(H) "Hessian not available. Either no value supplied or Laplace Approximation has not yet been estimated."
     return H + P₀
 end
@@ -70,7 +70,7 @@ end
 function log_likelihood(la::AbstractLaplace)
     factor = -_H_factor(la)
     if la.likelihood == :regression
-        c = la.posterior.n_data * la.posterior.n_out * log(la.prior.σ * sqrt(2 * pi))
+        c = la.posterior.n_data * la.posterior.n_out * log(la.prior.observational_noise * sqrt(2 * pi))
     else
         c = 0
     end
@@ -82,7 +82,7 @@ end
 
 Returns the factor σ⁻², where σ is used in the zero-centered Gaussian prior p(θ) = N(θ;0,σ²I)
 """
-_H_factor(la::AbstractLaplace) = 1 / (la.prior.σ^2)
+_H_factor(la::AbstractLaplace) = 1 / (la.prior.observational_noise^2)
 
 """
     _init_H(la::AbstractLaplace)
@@ -100,7 +100,7 @@ Smaller weights in a neural network can result in a model that is more stable an
 making a prediction on new data.
 """
 function _weight_penalty(la::AbstractLaplace)
-    μ = la.posterior.μ
+    μ = la.posterior.posterior_mean
     μ₀ = get_prior_mean(la)
     Δ = μ .- μ₀
     P₀ = prior_precision(la)
@@ -120,14 +120,14 @@ function log_marginal_likelihood(
 
     # update prior precision:
     if !isnothing(P₀)
-        la.prior.P₀ =
+        la.prior.prior_precision_matrix =
             typeof(P₀) <: AbstractFloat ? UniformScaling(P₀)(la.posterior.n_params) : P₀
     end
 
     # update observation noise:
     if !isnothing(σ)
-        @assert (la.likelihood == :regression || la.prior.σ == σ) "Can only change observational noise σ for regression."
-        la.prior.σ = σ
+        @assert (la.likelihood == :regression || la.prior.observational_noise == σ) "Can only change observational noise σ for regression."
+        la.prior.observational_noise = σ
     end
 
     return log_likelihood(la) - 0.5 * (log_det_ratio(la) + _weight_penalty(la))
@@ -147,7 +147,7 @@ end
 
 
 """
-log_det_prior_precision(la::AbstractLaplace) = sum(log.(diag(la.prior.P₀)))
+log_det_prior_precision(la::AbstractLaplace) = sum(log.(diag(la.prior.prior_precision_matrix)))
 
 """
     log_det_posterior_precision(la::AbstractLaplace)
diff --git a/src/direct_mlj.jl b/src/direct_mlj.jl
index 153f6a5..e8d1014 100644
--- a/src/direct_mlj.jl
+++ b/src/direct_mlj.jl
@@ -19,9 +19,9 @@ MLJBase.@mlj_model mutable struct LaplaceClassifier <: MLJBase.Probabilistic
     hessian_structure::Union{HessianStructure,Symbol,String} =
         :full::(_ in (:full, :diagonal))
     backend::Symbol = :GGN::(_ in (:GGN, :EmpiricalFisher))
-    σ::Float64 = 1.0
-    μ₀::Float64 = 0.0
-    P₀::Union{AbstractMatrix,UniformScaling,Nothing} = nothing
+    observational_noise::Float64 = 1.0
+    prior_mean::Float64 = 0.0
+    prior_precision_matrix::Union{AbstractMatrix,UniformScaling,Nothing} = nothing
     fit_prior_nsteps::Int = 100::(_ > 0)
     link_approx::Symbol = :probit::(_ in (:probit, :plugin))
 end
@@ -37,14 +37,32 @@ MLJBase.@mlj_model mutable struct LaplaceRegressor <: MLJBase.Probabilistic
     hessian_structure::Union{HessianStructure,Symbol,String} =
         :full::(_ in (:full, :diagonal))
     backend::Symbol = :GGN::(_ in (:GGN, :EmpiricalFisher))
-    σ::Float64 = 1.0
-    μ₀::Float64 = 0.0
-    P₀::Union{AbstractMatrix,UniformScaling,Nothing} = nothing
+    observational_noise::Float64 = 1.0
+    prior_mean::Float64 = 0.0
+    prior_precision_matrix::Union{AbstractMatrix,UniformScaling,Nothing} = nothing
     fit_prior_nsteps::Int = 100::(_ > 0)
 end
 
 LaplaceModels = Union{LaplaceRegressor,LaplaceClassifier}
 
+# Aliases
+const LM = LaplaceModels
+function Base.getproperty(ce::LM, sym::Symbol)
+    sym = sym === :σ ? :observational_noise : sym
+    sym = sym === :μ₀ ? :prior_mean : sym
+    sym = sym === :P₀ ? :prior_precision_matrix : sym
+    return Base.getfield(ce, sym)
+end
+function Base.setproperty!(ce::LM, sym::Symbol, val)
+    sym = sym === :σ ? :observational_noise : sym
+    sym = sym === :μ₀ ? :prior_mean : sym
+    sym = sym === :P₀ ? :prior_precision_matrix : sym
+    return Base.setfield!(ce, sym, val)
+end
+
+
+
+
 # for fit:
 function MMI.reformat(::LaplaceRegressor, X, y)
     return (MLJBase.matrix(X) |> permutedims, (reshape(y, 1, :), nothing))
@@ -193,9 +211,9 @@ function MMI.fit(m::LaplaceModels, verbosity, X, y)
         subnetwork_indices=m.subnetwork_indices,
         hessian_structure=m.hessian_structure,
         backend=m.backend,
-        σ=m.σ,
-        μ₀=m.μ₀,
-        P₀=m.P₀,
+        observational_noise=m.observational_noise,
+        prior_mean=m.prior_mean,
+        prior_precision_matrix=m.prior_precision_matrix,
     )
 
     if typeof(m) == LaplaceClassifier
@@ -282,9 +300,9 @@ function MMI.update(m::LaplaceModels, verbosity, old_fitresult, old_cache, X, y)
                 subnetwork_indices=m.subnetwork_indices,
                 hessian_structure=m.hessian_structure,
                 backend=m.backend,
-                σ=m.σ,
-                μ₀=m.μ₀,
-                P₀=m.P₀,
+                observational_noise=m.observational_noise,
+                prior_mean=m.prior_mean,
+                prior_precision_matrix=m.prior_precision_matrix,
             )
             if typeof(m) == LaplaceClassifier
                 la.likelihood = :classification
@@ -315,9 +333,9 @@ function MMI.update(m::LaplaceModels, verbosity, old_fitresult, old_cache, X, y)
         :subnetwork_indices,
         :hessian_structure,
         :backend,
-        :σ,
-        :μ₀,
-        :P₀,
+        :observational_noise,
+        :prior_mean,
+        :prior_precision_matrix,
     )
         println(" updating only the laplace optimization part")
         old_la = old_fitresult[1]
@@ -329,9 +347,9 @@ function MMI.update(m::LaplaceModels, verbosity, old_fitresult, old_cache, X, y)
             subnetwork_indices=m.subnetwork_indices,
             hessian_structure=m.hessian_structure,
             backend=m.backend,
-            σ=m.σ,
-            μ₀=m.μ₀,
-            P₀=m.P₀,
+            observational_noise=m.observational_noise,
+            prior_mean=m.prior_mean,
+            prior_precision_matrix=m.prior_precision_matrix,
         )
         if typeof(m) == LaplaceClassifier
             la.likelihood = :classification
@@ -452,10 +470,10 @@ end
 
  # Returns
  A named tuple containing:
- - `μ`: The mean of the posterior distribution.
+ - `mean`: The mean of the posterior distribution.
  - `H`: The Hessian of the posterior distribution.
  - `P`: The precision matrix of the posterior distribution.
- - `Σ`: The covariance matrix of the posterior distribution.
+ - `cov_matrix`: The covariance matrix of the posterior distribution.
  - `n_data`: The number of data points.
  - `n_params`: The number of parameters.
  - `n_out`: The number of outputs.
@@ -466,10 +484,10 @@ function MMI.fitted_params(model::LaplaceModels, fitresult)
     la, decode = fitresult
     posterior = la.posterior
     return (
-        μ=posterior.μ,
+        mean=posterior.posterior_mean,
         H=posterior.H,
         P=posterior.P,
-        Σ=posterior.Σ,
+        cov_matrix=posterior.posterior_covariance_matrix,
         n_data=posterior.n_data,
         n_params=posterior.n_params,
         n_out=posterior.n_out,
@@ -517,7 +535,7 @@ function MMI.predict(m::LaplaceModels, fitresult, Xnew)
         means, variances = yhat
 
         # Create Normal distributions from the means and variances
-        return vec([Normal(μ, sqrt(σ)) for (μ, σ) in zip(means, variances)])
+        return vec([Normal(mean, sqrt(variance)) for (mean, variance) in zip(means, variances)])
 
     else
         predictions =
@@ -551,7 +569,7 @@ MMI.metadata_pkg(
 MLJBase.metadata_model(
     LaplaceClassifier;
     input_scitype=Union{
-        AbstractMatrix{<:Union{MLJBase.Finite,MLJBase.Continuous}}, # matrix with mixed types
+        AbstractMatrix{<:Union{MLJBase.Finite,MLJBase.Infinite}}, # matrix with mixed types
         MLJBase.Table(MLJBase.Finite, MLJBase.Continuous), # table with mixed types
     },
     target_scitype=AbstractArray{<:MLJBase.Finite}, # ordered factor or multiclass
@@ -562,8 +580,8 @@ MLJBase.metadata_model(
 MLJBase.metadata_model(
     LaplaceRegressor;
     input_scitype=Union{
-        AbstractMatrix{<:Union{MLJBase.Finite,MLJBase.Continuous}}, # matrix with mixed types
-        MLJBase.Table(MLJBase.Finite, MLJBase.Continuous), # table with mixed types
+        AbstractMatrix{<:Union{MLJBase.Finite, MLJBase.Infinite}}, # matrix with mixed types
+        MLJBase.Table(MLJBase.Finite, MLJBase.Infinite), # table with mixed types
     },
     target_scitype=AbstractArray{MLJBase.Continuous},
     supports_training_losses=true,
@@ -626,11 +644,11 @@ Train the machine using `fit!(mach, rows=...)`.
 
 - `backend::Symbol = :GGN::(_ in (:GGN, :EmpiricalFisher))`:                                        the backend to use, either `:GGN` or `:EmpiricalFisher`.
 
-- `σ::Float64 = 1.0`:                                                                               the standard deviation of the prior distribution.
+- `observational_noise (alias σ)::Float64 = 1.0`:                                                   the standard deviation of the prior distribution.
 
-- `μ₀::Float64 = 0.0`:                                                                              the mean of the prior distribution.
+- `prior_mean (alias μ₀)::Float64 = 0.0`:                                                           the mean of the prior distribution.
 
-- `P₀::Union{AbstractMatrix,UniformScaling,Nothing} = nothing`:                                     the covariance matrix of the prior distribution.
+- `prior_precision_matrix (alias P₀)::Union{AbstractMatrix,UniformScaling,Nothing} = nothing`:      the covariance matrix of the prior distribution.
 
 - `fit_prior_nsteps::Int = 100::(_ > 0) `:                                                          the number of steps used to fit the priors.
 
@@ -650,13 +668,13 @@ Train the machine using `fit!(mach, rows=...)`.
 
 The fields of `fitted_params(mach)` are:
 
- - `μ`: The mean of the posterior distribution.
+ - `mean`: The mean of the posterior distribution.
 
  - `H`: The Hessian of the posterior distribution.
 
  - `P`: The precision matrix of the posterior distribution.
 
- - `Σ`: The covariance matrix of the posterior distribution.
+ - `cov_matrix`: The covariance matrix of the posterior distribution.
 
  - `n_data`: The number of data points.
 
@@ -766,11 +784,11 @@ Train the machine using `fit!(mach, rows=...)`.
 
 - `backend::Symbol = :GGN::(_ in (:GGN, :EmpiricalFisher))`:                                        the backend to use, either `:GGN` or `:EmpiricalFisher`.
 
-- `σ::Float64 = 1.0`:                                                                               the standard deviation of the prior distribution.
+- `observational_noise (alias σ)::Float64 = 1.0`:                                                   the standard deviation of the prior distribution.
 
-- `μ₀::Float64 = 0.0`:                                                                              the mean of the prior distribution.
+- `prior_mean (alias μ₀)::Float64 = 0.0`:                                                           the mean of the prior distribution.
 
-- `P₀::Union{AbstractMatrix,UniformScaling,Nothing} = nothing`:                                     the covariance matrix of the prior distribution.
+- `prior_precision_matrix (alias P₀)::Union{AbstractMatrix,UniformScaling,Nothing} = nothing`:      the covariance matrix of the prior distribution.
 
 - `fit_prior_nsteps::Int = 100::(_ > 0) `:                                                          the number of steps used to fit the priors.
 
@@ -789,13 +807,13 @@ Train the machine using `fit!(mach, rows=...)`.
 
 The fields of `fitted_params(mach)` are:
 
- - `μ`: The mean of the posterior distribution.
+ - `mean`: The mean of the posterior distribution.
 
  - `H`: The Hessian of the posterior distribution.
 
  - `P`: The precision matrix of the posterior distribution.
 
- - `Σ`: The covariance matrix of the posterior distribution.
+ - `cov_matrix`: The covariance matrix of the posterior distribution.
 
  - `n_data`: The number of data points.
 
diff --git a/src/full.jl b/src/full.jl
index 4720150..6cab8ec 100644
--- a/src/full.jl
+++ b/src/full.jl
@@ -46,7 +46,7 @@ function _fit!(
     la.posterior.H = H
     la.posterior.loss = loss
     la.posterior.P = posterior_precision(la)
-    la.posterior.Σ = posterior_covariance(la, la.posterior.P)
+    la.posterior.posterior_covariance_matrix = posterior_covariance(la, la.posterior.P)
     return la.posterior.n_data = n_data
 end
 
diff --git a/test/direct_mlj_interface.jl b/test/direct_mlj_interface.jl
index 6a0d642..f3bb228 100644
--- a/test/direct_mlj_interface.jl
+++ b/test/direct_mlj_interface.jl
@@ -14,6 +14,14 @@ cv = MLJBase.CV(; nfolds=3)
     flux_model = Chain(Dense(4, 10, relu), Dense(10, 10, relu), Dense(10, 1))
     model = LaplaceRegressor(; model=flux_model, epochs=20)
 
+    # Aliases:
+    model.σ = model.observational_noise
+    model.μ₀ = model.prior_mean
+    model.P₀ = model.prior_precision_matrix
+    @test model.observational_noise == model.σ
+    @test model.prior_mean == model.μ₀
+    @test model.prior_precision_matrix == model.P₀
+
     #testing more complex dataset
     X, y = MLJBase.make_regression(100, 4; noise=0.5, sparse=0.2, outliers=0.1)
     #train, test = partition(eachindex(y), 0.7); # 70:30 split
diff --git a/test/laplace.jl b/test/laplace.jl
index ea4cfbb..8994103 100644
--- a/test/laplace.jl
+++ b/test/laplace.jl
@@ -334,7 +334,7 @@ function run_workflow(
     la = Laplace(
         nn;
         likelihood=likelihood,
-        λ=λ,
+        prior_precision=λ,
         subset_of_weights=subset_of_weights,
         backend=backend,
         subnetwork_indices=subnetwork_indices,