"It gets easier… Every day it gets a little easier…
But you gotta do it every day - that’s the hard part.
But it does get easier"
BJH - S2 finale


Scientific publications


Articles in refereed journals


"Tiefe Brunnen muss man graben
wenn man klares Wasser will
Rosenrot oh Rosenrot
Tiefe Wasser sind nicht still"
Rosenrot - Rammstein


On Statistical Journals

  1. Varghese, A., Santos-Fernandez E., Denti, F., Mira, A., and Mergensen, K.
    A global perspective on the intrinsic dimensionality of COVID-19 data
    Scientific Reports, 2023


  2. Denti, F., Peluso, S., Guindani, M., and Mira, A.
    Multiple hypothesis screening using mixtures of non-local distributions
    Statistics in Medicine, 2023


  3. Denti, F.
    intRinsic: an R package for the package for model-based estimation of the intrinsic dimension of a dataset
    Journal of Statistical Software, 2023


  4. Denti, F., Azevedo, R., Lo, C., Wheeler, D. G., Gandhi, S.P.,Guindani, M., and Shahbaba, B.
    A Horseshoe mixture model for Bayesian screening with an application to light sheet fluorescence microscopy in brain imaging
    Annals of Applied Statistics, 2023 - In Press


  5. Denti, F., Doimo, D., Laio, A., and Mira, A.
    Gride: a novel likelihood-based intrinsic dimension estimator
    Scientific Reports, 2022


  6. Santos-Fernandez, E., Denti, F., Mengersen, K., and Mira A.
    The role of intrinsic dimension in high-resolution player tracking data – Insights in basketball
    Annals of Applied Statistics, 2021


  7. Denti, F., Camerlenghi, F., Guindani, M., and Mira, A.
    A Common Atom Model for the Bayesian Nonparametric Analysis of Nested Data
    Journal of the American Statistical Association, 2021


  8. Denti, F., Cappozzo, A., and Greselin, F.
    A Two-Stage Bayesian Semiparametric Model for Novelty Detection with Robust Prior Information
    Statistics and Computing, 2021


  9. Allegra, M., Facco, E. , Denti, F., Laio, A., and Mira, A.
    Data segmentation based on the local intrinsic dimension
    Scientific Reports, 2020


  10. Denti, F., Guindani, M., Leisen, F., Lioji, A., and Vannucci, M.
    Two-group Poisson-Dirichlet mixtures for multiple testing
    Biometrics, 2020


Collaborative Papers

  1. Migliavada, R., Ricci, F. Z., Denti, F., Haghverdian, D., and Torri, L.
    Is purchasing of vegetable dishes affected by organic or local labels? Empirical evidence from a university canteen
    Appetite, 2022


  2. Petazzoni, M., De Giacinto, E., Troiano, D., Denti, F., and Buiatti, M.
    Computed Tomographic Trochlear Depth Measurement in Normal Dogs
    Journal of Veterinary and Comparative Orthopaedics and Traumatology, 2018

Book chapters and proceedings


"Il testo che avrei voluto scrivere non è di certo questo
Perciò dovrò continuare a scrivere perché di certo riesco
(Prima o poi)"
Michele Salvemini - Translation


  1. D’Angelo, L., and Denti, F.
    Bayesian analysis of Amazon’s best-selling books via finite nested mixture models (pp. 1117-1120)
  2. Di Noia, A., Denti, F., and Mira, A.
    A tool for assessing weak identifiability of statistical models, (pp. 1230-1234)
  3. Denti, F., Di Noia, A., and Mira, A.
    Bayesian nonparametric estimation of heterogeneous intrinsic dimension via product partition models, (pp. 316-321)
  4. Capitoli, G., Colombara, S., Cotroneo, A., De Caro, F., Morandi, R., Schembri, C., Zapiola, A.G., and Denti, F.
    Detecting latent spatial patterns in mass spectrometry brain imaging data via Bayesian mixtures, (pp. 1127-1132)

    5. in F.M. Chelli, M. Ciommi, S. Ingrassia, F. Mariani, M.C. Recchioni (a cura di) Book of Short Papers SEAS IN 2023, Pearson.


  5. Denti, F., D’Angelo, L., and Guindani, M.
    Bayesian approaches for capturing the heterogeneity of neuroimaging experiments, (pp. 17-29)
  6. Denti, F., Camerlenghi, F., Guindani, M., and Mira, A.
    Clustering artists based on the energy distributions of their songs on Spotify via the Common Atoms Model, (pp. 121-126)
  7. Denti, F. and Mira, A.
    A tool to validate the assumptions on ratios of nearest neighbor distances: the Consecutive Ratio Paths, (pp. 1233-1238)

    8. in A. Balzanella, M. Bini, C. Cavicchia, and R. Verde (a cura di), Book of Short Papers SIS 2022, Pearson.


  8. Denti, F., Cappozzo, A., and Greselin, F.
    Outlier and novelty detection for Functional data: a semiparametric Bayesian approach
    in Book of Short Papers of the 5th international workshop on Models and Learning for Clustering and Classification, (Ingrassia A., Punzo A., Rocci R., editors) (pp. 33-38). Ledizioni.


  9. Denti, F., Cappozzo, A., and Greselin, F.
    Bayesian nonparametric adaptive classification with robust prior information
    in A. Pollice, N. Salvati, & F. Schirripa Spagnolo (a cura di), Book of Short Papers SIS 2020 (pp. 655-660). Pearson.


  10. Caponera, A., Denti, F., Rigon, T., Sottosanti, A., and Gelfand, A. Hierarchical Spatio-Temporal Modeling of Resting State fMRI Data
    in Studies in Neural Data Science (Canale, A., Durante, D., Paci, L., Scarpa, B., editors), 2018.

Submitted manuscripts


"Whoa, you know
to keep your hopes up high
And your head down low"
All I Want - A Day to Remember


  1. D’Angelo, L. and Denti, F.
    A finite-infinite shared atoms nested model for the Bayesian nonparametric analysis of large data sets - Submitted

  2. Benedetti, L., Boniardi, E., Chiani, L., Ghirri, J., Mastropietro, M., Cappozzo,A., and Denti, F.
    Variational Inference for Semiparametric Bayesian Novelty Detection in Large Datasets - Submitted

Software