Constraint Factorization Error

[mhidalgoaraya]

I have a model where an error occurs depending how I define the Naive Mean Field in the constrains:

it works if:
q(y, yprior, s, θ) = q(y)q(yprior)q(s)q(θ)

it doesn't work if:
q(y, yprior, s, θ) = MeanField()

`# Atomic Filter
@model function prob_lpf_switch(y, μx0, vx0, p∞, θ)
m = (0.0, 0.0)
yprior ~ Normal(mean = μx0, variance = vx0)
s ~ NormalMixture(switch = θ, m = m, p = p∞)
y := yprior + s
end

@model function prob_xnr_encap(y, π, μx0s, vx0s, μx0n, vx0n, vy, ps∞, pn∞)
	θ ~ Bernoulli(π)
    sp ~ prob_lpf_switch(μx0 = μx0s, vx0 = vx0s, p∞ = ps∞, θ = θ,)
    np ~ prob_lpf_switch(μx0 = μx0n, vx0 = vx0n, p∞ = pn∞, θ = θ,)	    

	g := sp - np
    g ~ Normal(m = 0.0, v = 1e10)
    
    y ~ Normal(m = sp, v = vy)
    y ~ Normal(m = np, v = vy)
end

function run_inference(data, π, vy, ps∞, pn∞)
    autoupdates = @autoupdates begin
        μx0s, vx0s = mean_var(q(sp))
        μx0n, vx0n = mean_var(q(np))
    end

    initialization = @initialization begin
        q(g) = vague(NormalMeanVariance)
		
		q(np) = vague(NormalMeanVariance)
		q(sp) = vague(NormalMeanVariance)
		
        for init in prob_lpf_switch
            q(y) = vague(NormalMeanVariance)
            q(s) = vague(NormalMeanVariance)
        end
    end

    constraints = @constraints begin
        for q in prob_lpf_switch
            q(y, yprior, s, θ) = MeanField()#q(y)q(yprior)q(s)q(θ)
        end
        q(sp, np, g) = MeanField()
    end

    rxengine = infer(
        model = prob_xnr_encap(vy = vy, ps∞ = ps∞, pn∞ = pn∞),
        autoupdates = autoupdates,
        data = (y = data, π = π),
        initialization = initialization,
        constraints = constraints,
        free_energy = false,
        showprogress = true,
        keephistory = 8000,
        historyvars = (
            sp = KeepLast(),
            np = KeepLast(),
			g = KeepLast(),
        ),
        returnvars = (:sp, :np, :g),
        autostart = true,
    )    
    return rxengine
end

`

[wouterwln]

This was a bug in GraphPPL and is fixed in version 4.3.3