CASTELLETTI, FEDERICO

Nome completo

Afferenza

DIPARTIMENTO DI STATISTICA E METODI QUANTITATIVI

Mostra records

Risultati 1 - 18 di 18 (tempo di esecuzione: 0.023 secondi).

Bayesian inference of graph-based dependencies from mixed-type data

2024 Galimberti, C; Peluso, S; Castelletti, F

Bayesian Learning of Causal Networks for Unsupervised Fault Diagnosis in Distributed Energy Systems

2024 Castelletti, F; Niro, F; Denti, M; Tessera, D; Pozzi, A

Bayesian learning of network structures from interventional experimental data

2024 Castelletti, F; Peluso, S

Bayesian Sample Size Determination for Causal Discovery

2024 Castelletti, F; Consonni, G

Joint structure learning and causal effect estimation for categorical graphical models

2024 Castelletti, F; Consonni, G; Della Vedova, M

Learning Bayesian Networks: A Copula Approach for Mixed-Type Data

2024 Castelletti, F

Bayesian graphical modeling for heterogeneous causal effects

2023 Castelletti, F; Consonni, G

Network structure learning under uncertain interventions

2023 Castelletti, F; Peluso, S

Discussion to: Bayesian graphical models for modern biological applications by Y. Ni, V. Baladandayuthapani, M. Vannucci and F.C. Stingo

2022 Castelletti, F; Consonni, G; La Rocca, L

Bayesian Causal Inference in Probit Graphical Models

2021 Castelletti, F; Consonni, G

Bayesian inference of causal effects from observational data in Gaussian graphical models

2021 Castelletti, F; Consonni, G

Equivalence class selection of categorical graphical models

2021 Castelletti, F; Peluso, S

Structural learning and estimation of joint causal effects among network-dependent variables

2021 Castelletti, F; Mascaro, A

Bayesian learning of multiple directed networks from observational data

2020 Castelletti, F; La Rocca, L; Peluso, S; Stingo, F; Consonni, G

Bayesian Model Selection of Gaussian Directed Acyclic Graph Structures

2020 Castelletti, F

Discovering causal structures in Bayesian Gaussian directed acyclic graph models

2020 Castelletti, F; Consonni, G

Learning Markov equivalence classes of Directed Acyclic Graphs: An objective bayes approach

2018 Castelletti, F; Consonni, G; Della Vedova, M; Peluso, S

Learning Markov Equivalence Classes of Gaussian DAGs via Observational and Interventional Data: an Objective Bayes Approach

2018 Castelletti, F

Titolo	Tipologia	Data di pubblicazione	Autori
Bayesian inference of graph-based dependencies from mixed-type data	01 - Articolo su rivista	2024	Galimberti C.Peluso S.Castelletti F.
Bayesian Learning of Causal Networks for Unsupervised Fault Diagnosis in Distributed Energy Systems	01 - Articolo su rivista	2024	Castelletti F.Niro F.Denti M.Tessera D.Pozzi A. + -
Bayesian learning of network structures from interventional experimental data	01 - Articolo su rivista	2024	Castelletti, FPeluso, S
Bayesian Sample Size Determination for Causal Discovery	01 - Articolo su rivista	2024	Castelletti F.Consonni G. + -
Joint structure learning and causal effect estimation for categorical graphical models	01 - Articolo su rivista	2024	Castelletti F.Consonni G.Della Vedova M. L. + -
Learning Bayesian Networks: A Copula Approach for Mixed-Type Data	01 - Articolo su rivista	2024	Castelletti F.
Bayesian graphical modeling for heterogeneous causal effects	01 - Articolo su rivista	2023	Castelletti F.Consonni G. + -
Network structure learning under uncertain interventions	01 - Articolo su rivista	2023	Castelletti, FedericoPeluso, Stefano
Discussion to: Bayesian graphical models for modern biological applications by Y. Ni, V. Baladandayuthapani, M. Vannucci and F.C. Stingo	01 - Articolo su rivista	2022	Castelletti F.Consonni G.La Rocca L. + -
Bayesian Causal Inference in Probit Graphical Models	01 - Articolo su rivista	2021	Castelletti F.Consonni G. + -
Bayesian inference of causal effects from observational data in Gaussian graphical models	01 - Articolo su rivista	2021	Castelletti F.Consonni G. + -
Equivalence class selection of categorical graphical models	01 - Articolo su rivista	2021	Castelletti F.Peluso S.
Structural learning and estimation of joint causal effects among network-dependent variables	01 - Articolo su rivista	2021	Castelletti F.Mascaro A.
Bayesian learning of multiple directed networks from observational data	01 - Articolo su rivista	2020	Castelletti F.La Rocca L.Peluso S.Stingo F. C.Consonni G. + -
Bayesian Model Selection of Gaussian Directed Acyclic Graph Structures	01 - Articolo su rivista	2020	Castelletti F.
Discovering causal structures in Bayesian Gaussian directed acyclic graph models	01 - Articolo su rivista	2020	Castelletti F.Consonni G. + -
Learning Markov equivalence classes of Directed Acyclic Graphs: An objective bayes approach	01 - Articolo su rivista	2018	Federico CastellettiGuido ConsonniMarco Della VedovaStefano Peluso + -
Learning Markov Equivalence Classes of Gaussian DAGs via Observational and Interventional Data: an Objective Bayes Approach	07 - Tesi di dottorato Bicocca post 2009	2018	CASTELLETTI, FEDERICO

Bicocca Open Archive

CASTELLETTI, FEDERICO

Bayesian inference of graph-based dependencies from mixed-type data

Bayesian Learning of Causal Networks for Unsupervised Fault Diagnosis in Distributed Energy Systems

Bayesian learning of network structures from interventional experimental data

Bayesian Sample Size Determination for Causal Discovery

Joint structure learning and causal effect estimation for categorical graphical models

Learning Bayesian Networks: A Copula Approach for Mixed-Type Data

Bayesian graphical modeling for heterogeneous causal effects

Network structure learning under uncertain interventions

Discussion to: Bayesian graphical models for modern biological applications by Y. Ni, V. Baladandayuthapani, M. Vannucci and F.C. Stingo

Bayesian Causal Inference in Probit Graphical Models

Bayesian inference of causal effects from observational data in Gaussian graphical models

Equivalence class selection of categorical graphical models

Structural learning and estimation of joint causal effects among network-dependent variables

Bayesian learning of multiple directed networks from observational data

Bayesian Model Selection of Gaussian Directed Acyclic Graph Structures

Discovering causal structures in Bayesian Gaussian directed acyclic graph models

Learning Markov equivalence classes of Directed Acyclic Graphs: An objective bayes approach

Learning Markov Equivalence Classes of Gaussian DAGs via Observational and Interventional Data: an Objective Bayes Approach