Publications

Our most recent publications:

2025

• An introduction to Sequential Monte Carlo for Bayesian inference and model comparison—with examples for psychology and behavioral science
  Max Hinne
  Behavior Research Methods 57 (5), 125, 2025

2024

• Non-Gaussian normative modelling with hierarchical Bayesian regression
  Augustijn AA de Boer et al.
  Imaging Neuroscience 2, 1-36, 2024
• Individual differences in processing speed and curiosity explain infant habituation and dishabituation performance
  Francesco Poli, Tommaso Ghilardi, Roseriet Beijers, Carolina de Weerth, Max Hinne, Rogier B Mars and Sabine Hunnius
  Developmental Science 27 (3), e13460, 2024
• Robust inference of dynamic covariance using Wishart processes and sequential Monte Carlo
  Hester Huijsdens, David Leeftink, Linda Geerligs and Max Hinne
  Entropy 26 (8), 695, 2024

2023

• PhenoScore quantifies phenotypic variation for rare genetic diseases by combining facial analysis with other clinical features using a machine-learning framework
  Alexander JM Dingemans et al.
  Nature Genetics 55 (9), 1598-1607, 2023
• Clustering children's learning behaviour to identify self-regulated learning support needs
  SHE Dijkstra, Max Hinne, Eliane Segers and Inge Molenaar
  Computers in Human Behavior 145, 107754, 2023
• Eight-month-old infants meta-learn by downweighting irrelevant evidence
  Francesco Poli, Tommaso Ghilardi, Rogier B Mars, Max Hinne and Sabine Hunnius
  Open Mind 7, 141-155, 2023

2022

• DeNovoCNN: a deep learning approach to de novo variant calling in next generation sequencing data
  Gelana Khazeeva et al.
  Nucleic Acids Research, 2022
• Phenotype based prediction of exome sequencing outcome using machine learning for neurodevelopmental disorders
  Alexander JM Dingemans et al.
  Genetics in Medicine 24 (3), 645-653, 2022
• Bayesian model averaging for nonparametric discontinuity design
  Max Hinne, David Leeftink, Marcel AJ van Gerven and Luca Ambrogioni
  Plos one 17 (6), e0270310, 2022

2021

• The JASP guidelines for conducting and reporting a Bayesian analysis
  Johnny Van Doorn et al.
  Psychonomic Bulletin & Review 28, 813-826, 2021
• Automatic structured variational inference
  Luca Ambrogioni, Kate Lin, Emily Fertig, Sharad Vikram, Max Hinne, Dave Moore and Marcel van Gerven
  International Conference on Artificial Intelligence and Statistics, 676-684, 2021
• Real time measuring of individual learners' self regulation during learning
  SHE Dijkstra, I Molenaar and M Hinne
  [Sl]: European Association for Research on Learning and Instruction (EARLI), 2021

2020

• A conceptual introduction to Bayesian model averaging
  Max Hinne, Quentin F Gronau, Don van den Bergh and Eric-Jan Wagenmakers
  Advances in Methods and Practices in Psychological Science 3 (2), 200-215, 2020
• A tutorial on conducting and interpreting a Bayesian ANOVA in JASP
  Don van den Bergh et al.
  L’Année psychologique 120 (1), 73-96, 2020
• The Bayesian Methodology of Sir Harold Jeffreys as a Practical Alternative to the P Value Hypothesis Test
  Alexander Ly et al.
  Computational Brain & Behavior 3, 153-161, 2020
• Validation of structural brain connectivity networks: The impact of scanning parameters
  Karen S Ambrosen et al.
  Neuroimage 204, 116207, 2020
• Highways to happiness for autistic adults? Perceived causal relations among clinicians
  Marie K Deserno, Denny Borsboom, Sander Begeer, Riet van Bork, Max Hinne and Hilde M Geurts
  Plos one 15 (12), e0243298, 2020
• Spectral discontinuity design: Interrupted time series with spectral mixture kernels
  David Leeftink and Max Hinne
  Machine Learning for Health, 213-225, 2020
• The Indian chefs process
  Patrick Dallaire, Luca Ambrogioni, Ludovic Trottier, Umut Güçlü, Max Hinne, Philippe Giguère, Marcel Gerven and François Laviolette
  Conference on Uncertainty in Artificial Intelligence, 600-608, 2020
• Tutorial para realizar e interpretar un análisis de la varianza bayesiana en JASP
  Don van den Bergh et al.
  L’Année psychologique 120 (1), 73-96, 2020

2019

• Node centrality measures are a poor substitute for causal inference
  Fabian Dablander and Max Hinne
  Scientific reports 9 (1), 6846, 2019
• Between-subject variability in the influence of mental imagery on conscious perception
  Nadine Dijkstra, Max Hinne, Sander Erik Bosch and MAJ Van Gerven
  Scientific reports 9 (1), 15658, 2019
• Forward amortized inference for likelihood-free variational marginalization
  Luca Ambrogioni, Umut Güçlü, Julia Berezutskaya, Eva Borne, Yaǧmur Güçlütürk, Max Hinne, Eric Maris and Marcel Gerven
  The 22nd International Conference on Artificial Intelligence and Statistics …, 2019
• SpikeCaKe: Semi-analytic nonparametric Bayesian inference for spike-spike neuronal connectivity
  Luca Ambrogioni, Patrick Ebel, Max Hinne, Umut Güçlü, Marcel Gerven and Eric Maris
  The 22nd International Conference on Artificial Intelligence and Statistics …, 2019
• Visual imagery strength and sensory ambiguity
  N Dijkstra, M Hinne, SE Bosch and MAJ van Gerven
  Radboud Data Repository, 2019