I am experimenting with mixtures of autoregressive models with the idea that the posterior on the mixture parameters should correspond roughly to “model selection” for the order of the autoregression. [aside: is “mixture” the right term here? It’s the same techniques but seems quite qualitatively different from e.g. a GMM since the purpose of the mixture is to choose just the size of the model]

For some background:

For an AR(p) model we have y(t) = \sum_{\tau = 1}^p b_p(t - \tau) y(t - \tau) + e_t. In a finite mixture I have basically that y \sim \theta_1 AR(1) + \cdots + \theta_{p_{max}} AR(p_{max}).

Each AR(p) model has a p-vector of coefficients AR(1): [b_1(1)], AR(2): [b_2(1), b_2(2)], \ldots. These can be parameterized in terms of reflection coefficients \Gamma_1, \ldots, \Gamma_p where \Gamma_1 determines b_1, the combination of \Gamma_1, \Gamma_2 determines (the 2-vector) b_2, etc.

It must hold that |\Gamma_\tau| < 1, so I’ve used a prior \frac{1}{2}(1 + \Gamma_\tau) \sim \beta(\mu_\tau, \nu_\tau), with various different experiments with the prior on \nu_\tau, e.g. \nu_\tau \sim \text{exp}(1) etc.

Fitting my model with p_{max} = 5 on data simulated from AR(2) actually seems to work reasonably well, except that it does not seem to do a good job of quantifying the certainty in the mixture coefficients \theta. In the below figure, \Gamma_1 and \Gamma_2 are very tightly concentrated on the correct value (I gave it a lot of data) and \Gamma_3, \ldots, \Gamma_5 have broad posteriors, which also makes sense since the data came from an AR(2) model.

My interpretation from this is that it “should” be “very confident” that the true model order is 2 – it seems clearly apparent in the relative disperson of \Gamma. However, this doesn’t seem to be reflected (at least to the extent that it is clear from the posterior on \Gamma) in the posteriors of \theta or especially of \nu_\tau.

After all that background explanation, my actual questions are:

If \Gamma_\tau \sim \beta(\mu_\tau, \nu_\tau) and the posterior of \Gamma_\tau is very concentrated [disperse] should that be clearly reflected in the posterior of \nu_\tau being (\nu_\tau is the pseudo-samples, which I understand should quantify uncertainty?) very large [small] ? And, to what extent could I correlate \nu with \theta in the prior (and would that be desirable)?

EDIT: There’s a labelling mistake – the plot shows the \log_{10}(\nu), which is why some are negative, but in any case, their posteriors are still very similar, despite the drastic differences in posteriors of \Gamma.

There’s a lot of code but here it is

```
/*
* A mixture of AR(p) models up to a maximum order p_max.
* Information on ragged datastructures in stan is relevant
*/
functions {
vector step_up(vector g){
/*
* Maps a vector of p reflection coefficients g (|g| < 1) to a
* stable sequence b of AR coefficients.
*/
int p = dims(g)[1]; // Model order
vector[p] b; // AR Coefficients
vector[p] b_cpy; // Memory
b[1] = g[1];
if(p == 1)
return -b;
for(k in 1:p - 1){
b_cpy = b;
for(tau in 1:k){
b[tau] = b_cpy[tau] + g[k + 1] * b_cpy[k - tau + 1];
}
b[k + 1] = g[k + 1];
}
return -b;
}
vector reverse(vector x){
int p = dims(x)[1];
vector[p] x_rev;
for (tau in 1:p)
x_rev[tau] = x[p - tau + 1];
return x_rev;
}
vector ar_model_forecast(vector y, vector y0, vector b, real sigma){
int p = dims(b)[1];
int T = dims(y)[1];
vector[T] y_hat;
vector[p] b_rev = reverse(b);
// Prepend the initial values y0
vector[p + T] y_full;
y_full[:p] = y0;
y_full[p + 1:] = y;
for(t in 1:T){
y_hat[t] = dot_product(b_rev, y_full[t:t + p - 1]);
}
return y_hat;
}
real ar_model_lpdf(vector y, vector y0, vector b, real sigma){
int T = dims(y)[1];
vector[T] y_hat = ar_model_forecast(y, y0, b, sigma);
return normal_lpdf(y | y_hat, sigma);
}
vector ar_model_rng(vector y, vector y0, vector b, real sigma){
int T = dims(y)[1];
vector[T] y_hat = ar_model_forecast(y, y0, b, sigma);
vector[T] y_rng;
for(t in 1:T)
y_rng[t] = normal_rng(y_hat[t], sigma);
return y_rng;
}
}
data {
int<lower=1> T; // Number of examples
int<lower=1> p_max; // The model order
simplex[p_max + 1] mu_th; // Dirichlet mean for theta
vector[T] y; // the data series y(t)
}
transformed data {
vector[p_max + T] t; // "time"
int n_params = (p_max * (p_max + 1)) / 2;
int model_size[p_max];
for(p in 1:p_max) // For a raged data structure
model_size[p] = p;
for(i in -p_max + 1:T)
t[i + p_max] = i;
t = t / T; // Normalize to between [-p/T, 1]
}
parameters {
real mu; // Mean value
real r; // Linear trend coefficient
vector[p_max] y0; // Initial values
simplex[p_max + 1] theta; // Mixture parameters
// vector<lower=0, upper=1>[p_max] mu_gamma; // Mean vector for gamma
real<lower=0> nu_th; // pseudo-samples for Dirichlet
vector<lower=0>[p_max] nu_gamma; // pseudo-samples gamma
vector<lower=-1, upper=1>[p_max] gamma; // Reflection coefficients
real<lower=0> sigma; // noise level
}
transformed parameters {
vector[n_params] b; // AR Coefficients
{int pos = 1;
// Compute the actual AR coefficients
for(p in 1:p_max){
b[pos:pos + model_size[p] - 1] = step_up(gamma[:p]);
pos += model_size[p];}}}
model {
vector[p_max] alpha; // transformed gamma prior params
vector[p_max] beta;
vector[p_max + 1] alpha_th; // transformed Dirichlet prior for theta
vector[p_max + 1] lpdfs; // mixture pdfs
vector[p_max + T] trend; // trend term
real mu_gamma = 0.5;
nu_th ~ inv_gamma(3, 3);
alpha_th = mu_th * nu_th;
theta ~ dirichlet(alpha_th);
// Noise level in the signal
sigma ~ normal(0, 5);
// Priors for the reflection coefficients
// mu_gamma ~ uniform(0, 1); // A vector
// nu_gamma ~ inv_gamma(3, 3);
nu_gamma ~ exponential(0.1);
alpha = mu_gamma * nu_gamma;
beta = (1 - mu_gamma) * nu_gamma;
// trend parameters
r ~ normal(0, 2); // The linear time coefficient
mu ~ normal(0, 2); // A mean offset
// The simple trend term
trend = mu + r * t;
// Initial values
y0 ~ normal(trend[:p_max], sigma);
// Mixture AR(p), including "AR(0)" (pure noise)
{int pos = 1;
vector[T] detrend_y = y - trend[p_max + 1:];
for(p in 1:p_max){
lpdfs[p] = theta[p] + ar_model_lpdf(detrend_y | y0[:model_size[p]],
b[pos:pos + model_size[p] - 1],
sigma);
pos += model_size[p];
}
lpdfs[p_max + 1] = theta[p_max + 1] + normal_lpdf(detrend_y | 0, sigma);
}
target += beta_lpdf(0.5 * (1 + gamma) | alpha, beta); // in (0, 1)
target += log_sum_exp(lpdfs);
}
generated quantities {
vector[T] y_ppc;
vector[T] trend; // trend term
int p = categorical_rng(theta);
int pos;
trend = mu + r * t[p_max + 1:];
y_ppc = trend;
if(p == 1){ // AR(1)
y_ppc += ar_model_rng(y, y0[:1], b[:1], sigma);
}
else if(p == p_max + 1){ // AR(0)
for(i in 1:T)
y_ppc[i] += normal_rng(0, sigma);
}
else{ // AR(p), p > 1
pos = 1 + sum(model_size[:p - 1]);
y_ppc += ar_model_rng(y, y0[:model_size[p]], b[pos:pos + model_size[p] - 1], sigma);
}
}
```