I have a simple model for which I have single data to be used in inference. I’m new to julia. Right now I’m setting up inference using turing.jl .
My Turing model looks like this :
@model function fit_(data1, prob) # Prior distributions. α ~ Uniform(0,0.1) β ~ Uniform(0,0.1) p = [ α, β] prob = remake(problem, p=p) predicted = solve(prob, Tsit5(), saveat=1)' for i in 1:6 data1[:,i] .~ MvNormal(predicted[:,i], σ^2 * I) end return nothing end model = fit_(z1 , problem) Do you guys think its a correct approach ? because model is simple and I inferred same model in r but here i'm not getting correct fitting except for first state in my model. Model: function new_model(du, u, p, t) x, s, a = u α, β = p du = - (α * x * s) du = β * x * s du = 0 end # true parameters these are random parameters for setting up problem p = [0.5, 0.2] # initial state values u0 = [20, 3, 2] problem = ODEProblem(new_model, u0, (0.0,10.0), p)