Created
May 25, 2020 13:17
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using DrWatson | |
@quickactivate "DynamicPPL_NeurIPS" | |
using Turing | |
using LinearAlgebra | |
using Random: seed! | |
seed!(1) | |
@model testmodel(p, O) = begin | |
x ~ Categorical(p) | |
if x == 1 | |
y ~ MvNormal(zeros(length(O)), 1.0) | |
for i in 1:length(O) | |
O[i] ~ Normal(y[i] * norm(y), 1.0) | |
end | |
elseif x == 2 | |
z ~ MvNormal(zeros(length(O)), 1.0) | |
for i in 1:length(O) | |
O[i] ~ Normal(z[i] * norm(z), 1.0) | |
end | |
else | |
k ~ MvNormal(zeros(length(O)), 1.0) | |
for i in 1:length(O) | |
O[i] ~ Normal(k[i] * norm(k), 1.0) | |
end | |
end | |
return O | |
end | |
# sample data from prior | |
N = 1000 | |
p = [0.25, 0.5, 0.25] | |
O = testmodel(p, fill(missing, N))() | |
model = testmodel(p, O) | |
# inference | |
particles = 3 | |
samples = 100 | |
spec_runs = 5 | |
chain = sample(model, PG(particles), samples, specialize_after = spec_runs) | |
# include("../infer_turing_dynamic.jl") | |
; |
Author
trappmartin
commented
May 25, 2020
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