The role of embedded integration rules in Bayesian statistics. - Ronald Cools; Petros Dellaportas (Stat. Comput., 1996)

On Bayesian model and variable selection using MCMC. - Petros Dellaportas; Jonathan J. Forster; Ioannis Ntzoufras (Stat. Comput., 2002)

Bayesian analysis of the unobserved ARCH model. - Stefanos G. Giakoumatos; Petros Dellaportas; Dimitris Nicolas Politis (Stat. Comput., 2005)

Multivariate mixtures of normals with unknown number of components. - Petros Dellaportas; Ioulia Papageorgiou (Stat. Comput., 2006)

Modelling nonlinearities and heavy tails via threshold normal mixture GARCH models. - D. Giannikis; Ioannis D. Vrontos; Petros Dellaportas (Comput. Stat. Data Anal., 2008)

Forecasting with non-homogeneous hidden Markov models. - Loukia Meligkotsidou; Petros Dellaportas (Stat. Comput., 2011)

Cholesky-GARCH models with applications to finance. - Petros Dellaportas; Mohsen Pourahmadi (Stat. Comput., 2012)

WGBSSuite: simulating whole-genome bisulphite sequencing data and benchmarking differential DNA methylation analysis tools. - Owen J. L. Rackham; Petros Dellaportas; Enrico Petretto; Leonardo Bottolo (Bioinform., 2015)

Fully Scalable Gaussian Processes using Subspace Inducing Inputs. - Aristeidis Panos; Petros Dellaportas; Michalis K. Titsias (CoRR, 2018)

Scalable Bayesian Learning for State Space Models using Variational Inference with SMC Samplers. - Marcel Hirt; Petros Dellaportas (AISTATS, 2019)

Copula-like Variational Inference. - Marcel Hirt; Petros Dellaportas; Alain Durmus (NeurIPS, 2019)

Gradient-based Adaptive Markov Chain Monte Carlo. - Michalis K. Titsias; Petros Dellaportas (NeurIPS, 2019)

Faster Gaussian Processes via Deep Embeddings. - Constantinos Daskalakis; Petros Dellaportas; Aristeidis Panos (CoRR, 2020)

Entropy-based adaptive Hamiltonian Monte Carlo. - Marcel Hirt; Michalis K. Titsias; Petros Dellaportas (NeurIPS, 2021)

Scalable and Interpretable Marked Point Processes. - Aristeidis Panos; Ioannis Kosmidis; Petros Dellaportas (CoRR, 2021)

Large scale multi-label learning using Gaussian processes. - Aristeidis Panos; Petros Dellaportas; Michalis K. Titsias (Mach. Learn., 2021)

How Good Are Low-Rank Approximations in Gaussian Process Regression? - Constantinos Daskalakis; Petros Dellaportas; Aristeidis Panos (AAAI, 2022)

Scalable marked point processes for exchangeable and non-exchangeable event sequences. - Aristeidis Panos; Ioannis Kosmidis; Petros Dellaportas (AISTATS, 2023)

Sparse Spectral Bayesian Permanental Process with Generalized Kernel. - Jeremy Sellier; Petros Dellaportas (AISTATS, 2023)

Bayesian online change point detection with Hilbert space approximate Student-t process. - Jeremy Sellier; Petros Dellaportas (ICML, 2023)

Variance reduction for Metropolis-Hastings samplers. - Angelos Alexopoulos; Petros Dellaportas; Michalis K. Titsias (Stat. Comput., 2023)

Probabilistic Multi-Layer Perceptrons for Wind Farm Condition Monitoring. - Filippo Fiocchi; Domna Ladopoulou; Petros Dellaportas (CoRR, 2024)

Can independent Metropolis beat crude Monte Carlo? - Siran Liu; Petros Dellaportas; Michalis K. Titsias (CoRR, 2024)

Bayesian tensor factorisations for time series of counts. - Zhongzhen Wang; Petros Dellaportas; Ioannis Kosmidis (Mach. Learn., 2024)

Learning variational autoencoders via MCMC speed measures. - Marcel Hirt; Vasileios Kreouzis; Petros Dellaportas (Stat. Comput., 2024)

Probabilistic Wind Power Forecasting via Non-Stationary Gaussian Processes. - Domniki Ladopoulou; Dat Minh Hong; Petros Dellaportas (CoRR, 2025)

Gaussian Invariant Markov Chain Monte Carlo. - Michalis K. Titsias; Angelos Alexopoulos; Siran Liu; Petros Dellaportas (CoRR, 2025)