Sampling Steps

NUTS

aemcmc.nuts.step(srng, to_sample_rvs, realized_rvs_to_values)

Build a NUTS sampling step and its initial state.

This sampling step works with variables in their original space, to create the sampling step we thus need to:

1. Create the initial value for the variables to sample;

2. Create a log-density graph that works in the transformed space, and build a NUTS kernel that uses this graph;

3. Apply the default transformations to the initial values;

4. Apply the NUTS kernel to the transformed initial values;

5. Apply the backward transformation to the updated values.

Parameters:

• rvs_to_samples – A dictionary that maps the random variables whose posterior distribution we wish to sample from to their initial values.

• realized_rvs_to_values – A dictionary that maps the random variables not sampled by NUTS to their realized value. These variables can either correpond to observations, or to variables whose value is set by a different sampler.

Returns:

• A NUTS sampling step for each random variable, their initial values, the

• shared variable updates and the NUTS parameters.

FFBS

aemcmc.ffbs.ffbs_step(gamma_0, Gammas, log_lik, srng, lower_prec_bound=1e-20)

Draw a discrete state sequence sample using forward-filtering backward-sampling (FFBS) [fs].

FFBS draws samples according to

\[\begin{split}S_T &\sim p(S_T | y_{1:T}) \\ S_t \mid S_{t+1} &\sim p(S_{t+1} | S_t) p(S_{t+1} \mid y_{1:T})\end{split}\]

for discrete states in the sequence \(S_t\), \(t \in \{0, \dots, T\}\) and observations \(y_t\).

The argument gamma_0 corresponds to \(S_0\), Gammas to \(p(S_{t+1} | S_t)\), and log_lik to \(\log p(y_t \mid S_t)\). The latter is used to derive the forward quantities \(p(S_{t+1} \mid y_{1:T})\).

This implementations is most similar to the one in [nr], which forgoes expensive log-space calculations by using fixed precision bounds and re-normalization/scaling.

Parameters:

• gamma_0 – The initial state probabilities as an array of base shape (M,).

• Gamma – The transition probability matrices. This array should take the base shape (N, M, M), where N is the state sequence length and M is the number of distinct states. If N is 1, the single transition matrix will broadcast across all elements of the state sequence.

• log_lik – An array of base shape (M, N) consisting of the log-likelihood values for each state value at each point in the sequence.

• lower_prec_bound – A number indicating when forward probabilities are too small and need to be re-normalized/scaled.

Returns:

• A tuple containing the tensor representing the FFBS sampled states and the

• Scan-generated updates.

References

[fs]

Sylvia Fruehwirth-Schnatter, “Markov chain Monte Carlo estimation of classical and dynamic switching and mixture models”, Journal of the American Statistical Association 96 (2001): 194–209

[nr]

Press, William H., Saul A. Teukolsky, William T. Vetterling, and Brian P. Flannery. 2007. “Numerical Recipes 3rd Edition: The Art of Scientific Computing.” Cambridge university press.

Gibbs

aemcmc.gibbs.normal_regression_posterior(srng, omega, lmbdatau_inv, X, z)

Sample from the posterior of a normal prior and normal observation model.

\[\begin{split}\begin{align*} Z &\sim \operatorname{N}\left( X \beta, \Omega^{-1} \right) \\ \beta &\sim \operatorname{N}\left( 0, \tau^2 \Lambda \right) \end{align*}\end{split}\]

where \(X \in \mathbb{R}^{n \times p}\), \(\Lambda = \operatorname{diag}\left(\lambda^2_1, \dots, \lambda^2_p\right)\), and \(\Omega^{-1} = \operatorname{diag}\left(\omega_1, \dots, \omega_n\right)\).

The posterior distribution of \(\beta\) is given by

\[\begin{split}\begin{align*} \left( \beta \mid Z = z \right) &\sim \operatorname{N}\left( A^{-1} X^{\top} \Omega z, A^{-1} \right) \\ A &= X^{\top} X + \Lambda^{-1}_{*} \\ \Lambda_{*} &= \tau^2 \Lambda \end{align*}\end{split}\]

This function chooses the best sampler for \(\beta \mid z\) based on the dimensions of \(X\).

Parameters:

• srng – The random number generator used to draw samples.

• omega – The observation model diagonal std. dev. values \(\omega_i\). In other words, \(\operatorname{diag}\left(\Omega\right)\).

• lmbdatau_inv – The inverse \(beta\) std. dev. values \(\tau^{-1} \lambda^{-1}_i\). In other words, \(\operatorname{diag}\left(\Lambda^{-1/2}_{*}\right)\).

• X – Regression matrix \(X\).

• z – Observed values \(z \sim Z\).

Return type:

A sample from \(\beta \mid z\).

aemcmc.gibbs.horseshoe_posterior(srng, beta, sigma2, lambda2, tau2)

Gibbs kernel to sample from the posterior distributions of the horseshoe prior shrinkage parameters.

This kernel generates samples from the posterior distribution of the local and global shrinkage parameters of a horseshoe prior, respectively \(\lambda\) and \(\tau\) in the following model:

\[\begin{split}\begin{align*} \beta_j &\sim \operatorname{N}\left(0, \lambda_j^2 \tau^2 \sigma^2\right) \\ \lambda_j &\sim \operatorname{C}^{+}(0, 1) \\ \tau &\sim \operatorname{C}^{+}(0, 1) \end{align*}\end{split}\]

The graphs constructed by this function are \(\lambda \mid \beta, \tau\) and \(\tau \mid \lambda\), respectively.

We use the following observations [1]_ to sample from the posterior conditional probability of \(\tau\) and \(\lambda\):

1. The half-Cauchy distribution can be intepreted as a mixture of inverse-gamma distributions; 2. If \(Z \sim \operatorname{InvGamma}(1, a)\), \(Z \sim 1 / \operatorname{Exp}(a)\).

Parameters:

• srng – The random number generating object to be used during sampling.

• beta – Regression coefficients.

• sigma2 – Variance of the regression coefficients.

• lambda2 – Square of the local shrinkage parameters.

• tau2 – Square of the global shrinkage parameters.

Return type:

Posteriors for \(lambda\) and \(tau\), respectively.

References

[1]

Makalic, Enes & Schmidt, Daniel. (2016). High-Dimensional Bayesian Regularised Regression with the BayesReg Package.

aemcmc.gibbs.nbinom_dispersion_posterior(srng, h, p, a, b, y)

Sample the conditional posterior for the dispersion parameter under a negative-binomial and gamma prior.

In other words, this draws a sample from \(h \mid Y = y\) per

\[\begin{split}\begin{align*} Y_i &\sim \operatorname{NB}(h, p_i) \\ h &\sim \operatorname{Gamma}(a, b) \end{align*}\end{split}\]

where \(\operatorname{NB}\) is a negative-binomial distribution.

The conditional posterior sample step is derived from the following decomposition:

\[\begin{gather*} Y_i = \sum_{j=1}^{l_i} u_{i j}, \quad u_{i j} \sim \operatorname{Log}(p_i), \quad l_i \sim \operatorname{Pois}\left(-h \log(1 - p_i)\right) \end{gather*}\]

where \(\operatorname{Log}\) is the logarithmic distribution. Under a gamma prior, \(h\) is conjugate to \(l\). We draw samples from \(l\) according to \(l \sim \operatorname{CRT(y, h)}\), where \(y\) is a sample from \(y \sim Y\).

The resulting posterior is

\[\begin{gather*} \left(h \mid Y = y\right) \sim \operatorname{Gamma}\left(a + \sum_{i=1}^N l_i, \frac{1}{1/b + \sum_{i=1}^N \log(1 - p_i)} \right) \end{gather*}\]

Parameters:

• srng – The random number generator from which samples are drawn.

• h – The value of \(h\).

• p – The success probability parameter in the negative-binomial distribution of \(Y\).

• a – The shape parameter in the \(\operatorname{Gamma}\) prior on \(h\).

• b – The rate parameter in the \(\operatorname{Gamma}\) prior on \(h\).

• y – A sample from \(Y\).

Return type:

A sample from the posterior \(h \mid y\).

References

[1]

Zhou, Mingyuan, Lingbo Li, David Dunson, and Lawrence Carin. 2012. “Lognormal and Gamma Mixed Negative Binomial Regression.” Proceedings of the International Conference on Machine Learning. 2012: 1343–50. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4180062/.

aemcmc.gibbs.nbinom_normal_posterior(srng, beta, beta_std, X, h, y)

Produce a Gibbs sample step for a negative binomial logistic-link regression with a normal prior.

The implementation follows the sampler described in [1]_. It is designed to sample efficiently from the following negative binomial regression model:

\[\begin{split}\begin{align*} Y &\sim \operatorname{NB}\left(h, p\right) \\ p &= \frac{\exp(\psi)}{1 + \exp(\psi)} \\ \psi &= X^\top \beta \\ \beta &\sim \operatorname{N}(0, \lambda^2) \\ \end{align*}\end{split}\]

where \(\operatorname{NB}\) is a negative-binomial distribution.

Parameters:

• srng – The random number generator from which samples are drawn.

• beta – The current/previous value of the regression parameter \(beta\).

• beta_std – The std. dev. of the regression parameter \(beta\).

• X – The regression matrix.

• h – The \(h\) parameter in the negative-binomial distribution of \(Y\).

• y – A sample from the observation distribution \(y \sim Y\).

Return type:

A sample from the posterior \(\beta \mid y\).

Notes

The \(z\) expression in Section 2.2 of [1]_ seems to omit division by the Polya-Gamma auxilliary variables whereas [2] and [3] explicitly include it. We found that including the division results in accurate posterior samples for the regression coefficients. It is also worth noting that the \(\sigma^2\) parameter is not sampled directly in the negative binomial regression problem and thus set to 1 [2].

References

[1]

Makalic, Enes & Schmidt, Daniel. (2015). A Simple Sampler for the Horseshoe Estimator. 10.1109/LSP.2015.2503725.

[2] (1,2)

Makalic, Enes & Schmidt, Daniel. (2016). High-Dimensional Bayesian Regularised Regression with the BayesReg Package.

[3]

Neelon, Brian. (2019). Bayesian Zero-Inflated Negative Binomial Regression Based on Pólya-Gamma Mixtures. Bayesian Anal. 2019 September ; 14(3): 829–855. doi:10.1214/18-ba1132.

aemcmc.gibbs.bern_normal_posterior(srng, beta, beta_std, X, y)

Produce a Gibbs sample step for a bernoulli logistic-link regression with a normal prior.

The implementation follows the sampler described in [1]_. It is designed to sample efficiently from the following binary logistic regression model:

\[\begin{split}\begin{align*} Y &\sim \operatorname{Bern}\left( p \right) \\ p &= \frac{1}{1 + \exp\left( -X^\top \beta\right)} \\ \beta_j &\sim \operatorname{N}\left( 0, \lambda_j^2 \right) \end{align*}\end{split}\]

Parameters:

• beta – The current/previous value of the regression parameter \(beta\).

• beta_std – The std. dev. of the regression parameter \(beta\).

• X – The regression matrix.

• y – A sample from the observation distribution \(y \sim Y\).

Return type:

A sample from \(\beta \mid y\).

References

[1]

Makalic, Enes & Schmidt, Daniel. (2016). High-Dimensional Bayesian Regularised Regression with the BayesReg Package.