Numerical Aspects of Inertial Navigation. - Milan Papez; Petr Pivonka (PDeS, 2013)

Comparison of Kalman filters formulated as the statistics of the Normal-inverse-Wishart distribution. - Jakub Dokoupil; Milan Papez; Pavel Vaclavek (CDC, 2015)

On Bayesian decision-making and approximation of probability densities. - Milan Papez (TSP, 2015)

Sequential Monte Carlo estimation of transition probabilities in mixture filtering problems. - Milan Papez (FUSION, 2016)

A Rao-Blackwellized particle filter to estimate the time-varying noise parameters in non-linear state-space models using alternative stabilized forgetting. - Milan Papez (ISSPIT, 2016)

Rao-Blackwellized Particle Gibbs Kernels for Smoothing in Jump Markov Nonlinear Models. - Milan Papez (ECC, 2018)

Dynamic Bayesian Knowledge Transfer Between a Pair of Kalman Filters. - Milan Papez; Anthony Quinn (MLSP, 2018)

A Projection-Based Rao-Blackwellized Particle Filter to Estimate Parameters in Conditionally Conjugate State-Space Models. - Milan Papez (SSP, 2018)

Bayesian transfer learning between Gaussian process regression tasks. - Milan Papez; Anthony Quinn (ISSPIT, 2019)

Robust Bayesian Transfer Learning Between Kalman Filters. - Milan Papez; Anthony Quinn (MLSP, 2019)

Bayesian transfer learning between Student-t filters. - Milan Papez; Anthony Quinn (Signal Process., 2020)

Parameter Estimation for a Jump Diffusion Model of Type 2 Diabetic Patients in the Presence of Unannounced Meals. - Mohamad Al Ahdab; Milan Papez; Torben Knudsen; Tinna Björk Aradóttir; Signe Schmidt; Kirsten Nørgaard; John Leth (CCTA, 2021)

Fitting large mixture models using stochastic component selection. - Milan Papez; Tomás Pevný; Václav Smídl (CoRR, 2021)

Transferring model structure in Bayesian transfer learning for Gaussian process regression. - Milan Papez; Anthony Quinn (Knowl. Based Syst., 2022)

Sum-Product-Set Networks: Deep Tractable Models for Tree-Structured Graphs. - Milan Papez; Martin Rektoris; Václav Smídl; Tomás Pevný (ICLR, 2024)

GraphSPNs: Sum-Product Networks Benefit From Canonical Orderings. - Milan Papez; Martin Rektoris; Václav Smídl; Tomás Pevný (CoRR, 2024)

Probabilistic Graph Circuits: Deep Generative Models for Tractable Probabilistic Inference over Graphs. - Milan Papez; Martin Rektoris; Václav Smídl; Tomás Pevný (UAI, 2025)

Sparse Probabilistic Graph Circuits. - Martin Rektoris; Milan Papez; Václav Smídl; Tomás Pevný (CoRR, 2025)