The Complexity of Pebbling in Diameter Two Graphs. - Charles A. Cusack; Timothy Lewis; Daniel Simpson; Samuel Taggart (SIAM J. Discret. Math., 2012)

Bayesian computing with INLA: New features. - Thiago G. Martins; Daniel Simpson; Finn Lindgren; Håvard Rue (Comput. Stat. Data Anal., 2013)

On the Convergence of the Escalator Boxcar Train. - Åke Brännström; Linus Carlsson; Daniel Simpson (SIAM J. Numer. Anal., 2013)

The Prior Can Often Only Be Understood in the Context of the Likelihood. - Andrew Gelman; Daniel Simpson; Michael Betancourt (Entropy, 2017)

Yes, but Did It Work?: Evaluating Variational Inference. - Yuling Yao; Aki Vehtari; Daniel Simpson; Andrew Gelman (ICML, 2018)

Asynchronous Gibbs Sampling. - Alexander Terenin; Daniel Simpson; David Draper (AISTATS, 2020)

Hamiltonian Monte Carlo using an adjoint-differentiated Laplace approximation: Bayesian inference for latent Gaussian models and beyond. - Charles C. Margossian; Aki Vehtari; Daniel Simpson; Raj Agrawal (NeurIPS, 2020)

Thermodynamic Linear Algebra. - Maxwell Aifer; Kaelan Donatella; Max Hunter Gordon; Thomas Ahle; Daniel Simpson; Gavin E. Crooks; Patrick J. Coles (CoRR, 2023)

The Integrated Nested Laplace Approximation for Fitting Dirichlet Regression Models. - Joaquín Martínez-Minaya; Finn Lindgren; Antonio López-Quílez; Daniel Simpson; David V. Conesa (J. Comput. Graph. Stat., 2023)

Explainable Deep Learning Models for Dynamic and Online Malware Classification. - Quincy Card; Daniel Simpson; Kshitiz Aryal; Maanak Gupta; Sheikh Rabiul Islam (SMARTCOMP, 2024)

Pareto Smoothed Importance Sampling. - Aki Vehtari; Daniel Simpson; Andrew Gelman; Yuling Yao; Jonah Gabry (J. Mach. Learn. Res., 2024)

Bayesian cross-validation by parallel Markov chain Monte Carlo. - Alex Cooper; Aki Vehtari; Catherine Forbes; Daniel Simpson; Lauren Kennedy (Stat. Comput., 2024)

Scalable Bayesian Learning with posteriors. - Samuel Duffield; Kaelan Donatella; Johnathan Chiu; Phoebe Klett; Daniel Simpson (ICLR, 2025)