clean up further merging issues

EpiAware/src/latentmodels/randomwalk.jl

@@ -20,90 +20,3 @@ end
     end
     return rw, (; σ_RW, rw_init)
 end
-
-# Define the Priors type alias
-const Priors = Union{Distribution, Vector{<:Distribution}, Product}
-
-struct AR <: AbstractLatentModel
-    """A distribution representing the prior distribution of the damping factors."""
-    damp_prior::Priors
-
-    """A distribution representing the prior distribution of the variance."""
-    var_prior::Distribution
-
-    """A distribution representing the prior distribution of the initial values."""
-    init_prior::Priors
-
-    """
-    The order of the AR process, determined by the length of `damp_prior`.
-    """
-    p::Int
-
-    function AR(damp_prior::Priors, var_prior::Distribution, init_prior::Priors)
-        p = length(damp_prior)
-        return new(damp_prior, var_prior, init_prior, p)
-    end
-
-    function AR(damp_prior::Priors, var_prior::Distribution, init_prior::Priors, p::Int)
-        @assert length(init_prior)==p "Dimension of init_prior must be equal to the order of the AR process"
-        return new(damp_prior, var_prior, init_prior, p)
-    end
-end
-
-function default_ar_priors()
-    return (:damp_prior => [truncated(Normal(0.0, 0.05), 0.0, 1)],
-        :var_prior => truncated(Normal(0.0, 0.05), 0.0, Inf),
-        :init_prior => Normal()) |> Dict
-end
-
-@model function generate_latent(latent_model::AR, n)
-    p = latent_model.p
-    ϵ_t ~ MvNormal(ones(n - p))
-    σ²_AR ~ latent_model.var_prior
-    ar_init ~ latent_model.init_prior
-    damp_AR ~ latent_model.damp_prior
-    σ_AR = sqrt(σ²_AR)
-
-    @assert n>p "n must be longer than p"
-
-    # Initialize the AR series with the initial values
-    ar = Vector{Float64}(undef, n)
-    ar[1:p] = ar_init
-
-    # Generate the rest of the AR series
-    for t in (p + 1):n
-        ar[t] = damp_AR' * ar[(t - p):(t - 1)] + σ_AR * ϵ_t[t - p]
-    end
-
-    return ar, (; σ_AR, ar_init, damp_AR)
-end
-
-struct DiffLatentModel{T <: AbstractModel} <: AbstractLatentModel
-    model::T
-    d::Int
-    init_prior::Priors
-
-    function DiffLatentModel(model::T, init_prior::Priors)
-        d = length(init_prior)
-        return new(model, d, init_prior)
-    end
-
-    function DiffLatentModel(model::T, d::Int, init_prior::Priors)
-        @assert d>0 "d must be greater than 0"
-        @assert length(init_prior)==d "Length of init_prior must be equal to d"
-        return new(model, d, init_prior)
-    end
-end
-
-@model function generate_latent(latent_model::DiffLatentModel, n)
-    d = latent_model.d
-    @assert n>d "n must be longer than d"
-    init_latent ~ latent_model.init_prior
-
-    @submodel diff_latent, diff_latent_aux = generate_latent(latent_model.model, n - d)
-
-    latent = vcat(init_latent, diff_latent) |>
-             cumsum
-    # Return the reconstructed series and the parameters
-    return latent, (; init_latent, diff_latent_aux...)
-end