# Autoregressive Model

Hello everyone,

I start saying that I am very new both to PyMC3 and Bayesian statistical modelling.
What I was trying to do was to write an AR(1) model for fitting the toy data produced by this function

``````def generate_poisson_ar(lam_int, slope_a, slope_b, mu_noise, sigma_noise,
burn_factor=2, time_steps=48):
intercept = np.random.poisson(lam_int)
slope = np.random.beta(slope_a, slope_b)
process = [intercept]
true_parameters = {
'Intercpet': intercept,
'Slope': slope
}
for time_step in range(time_steps * burn_factor):

new_value = intercept + int(slope * process[time_step]) + \
np.random.normal(mu_noise, sigma_noise)
new_value = max(0, new_value)
process.append(new_value)

process = np.array(process[-time_steps:])
return process, true_parameters

process, true_parameters = generate_poisson_ar(
lam_int=200,
slope_a=2,
slope_b=5,
mu_noise=50,
sigma_noise=50
)
X = process[:-1]
y = process[1:]
``````

But instead of using the PyMC3 `AR1` class I wrote this model:

``````prior_mu = 200
prior_alpha = 2
prior_beta = 5
with pm.Model() as ar_model:

intercept = pm.Poisson(
mu=prior_mu,
name='Intercept'
)
slope = pm.Beta(
alpha=prior_alpha,
beta=prior_beta,
name='Slope'
)

mu = intercept + slope*X

outcome = pm.Poisson(
mu=mu,
observed=y,
name='y'
)
``````

My questions are:

1. Do you think the model I defined is a sensible alternative to use the example provided on the PyMC3 docs (the one using the `AR1` class)?
2. Inspecting both the traceplot and the the posterior predictions

made by the model it seems that my solution work ok-ish (the MCMC chains are not very well mixed), however I get these errors:
``````Sampling 4 chains for 2_000 tune and 1_000 draw iterations (8_000 + 4_000 draws total) took 72 seconds.
The acceptance probability does not match the target. It is 0.9144978498054566, but should be close to 0.8. Try to increase the number of tuning steps.
The estimated number of effective samples is smaller than 200 for some parameters.
``````

Can anyone help me shading some light on the reasons behind these messages?

Thank you

1 Like

Hi,
I know its been over a year so maybe this answer isnâ€™t as useful but posting here for posterityâ€™s sake. Those are MCMC sampler diagnostics that are warning that the sampler may have run into some trouble. This other thread provides more clarification as to how to interpret the messages.

Hi

I am now (a tiny bit) more versed in PyMC3 and I realized several issues with my approach. Nevertheless you answer has been very useful!

Thank you
Valerio