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Scheda Riassuntiva
Anno Accademico 2017/2018
Scuola Scuola di Ingegneria Industriale e dell'Informazione
Insegnamento 098516 - NUMERICAL AND STATISTICAL METHODS IN GEOSCIENCES
  • 098515 - NUMERICAL AND STATISTICAL METHODS IN GEOSCIENCES [2]
Docente Vantini Simone
Cfu 4.00 Tipo insegnamento Modulo Di Corso Strutturato

Corso di Studi Codice Piano di Studio preventivamente approvato Da (compreso) A (escluso) Insegnamento
Ing - Civ (Mag.)(ord. 270) - MI (489) INGEGNERIA PER L'AMBIENTE E IL TERRITORIO - ENVIRONMENTAL AND LAND PLANNING ENGINEERING*AZZZZ098516 - NUMERICAL AND STATISTICAL METHODS IN GEOSCIENCES
Ing Ind - Inf (Mag.)(ord. 270) - MI (487) MATHEMATICAL ENGINEERING - INGEGNERIA MATEMATICA*AZZZZ098516 - NUMERICAL AND STATISTICAL METHODS IN GEOSCIENCES

Programma dettagliato e risultati di apprendimento attesi

Objectives.
The course aims at providing students with statistical tools for the analysis of data typically encountered in geo-science and environmental applications. The course is organized in theoretical classes and laboratories. Basic knowledge in probability and statistics at bachelor level is suggested.

 

Program.

  • Euclidean Multivariate Data (brief review). The Euclidean geometry in the real space, Principal Component Analysis, clustering, permutational one- and two-population tests.
  • Compositional Data (e.g., chemical compounds, mineralogical compositions, atmospheric pollutants). The Aitchinson geometry in the simplex, transformations of compositional data, Principal Component Analysis of compositional data, clustering of compositional data, permutational one- and two-population tests for compositional data.
  • Directional Data (e.g, winds, waves, geological fault directions).The geodesic distance, Principal Component Analysis of directional data, clustering of directional data, permutational one- and two-population tests for directional data.
  • Tensor Data (e.g., diffusion of oil, water, vehicles and people). Distances between tensors, clustering of tensor data, permutational one- and two-population tests for tensor data.
  • Network Data (e.g., river networks, oil and gas pipelines, mobility networks). Network representations (adjacency, Laplacian, and modularity matrix), distances between networks, clustering of network data, permutational one- and two-population tests for network data.
  • Data with Spatial and/or Temporal Dependence. Measures of spatial dependence, covariogram and variogram, estimation and prediction via ordinary and universal Kriging, hidden Markov random fields.

 

Course Organization.
The course is made of theoretical lectures (24 hours) followed by lab sessions (16 hours). During the theoretical lectures methods and algorithms will be presented in the proper mathematical framework. During the lab sessions methods and algorithms will be instead illustrated and tested through applications to real data sets. The analyses performed during the lab sessions will be carried out by means the opensource software R (www.r-project.org). Along the course, students are expected to work on a data analysis team project.

 

Evaluation Criteria.
The exam consists of the oral presentation of the team project.

 

Bibliography.

  • V. Pawlowsky-Glahn and A. Buccianti. Compositional data analysis. Theory and applications. John Wiley and Sons Ltd., 2011.
  • K. V. Mardia and P. Jupp, Directional Statistics (2nd edition), John Wiley and Sons Ltd., 2000.    
  • P. I. Good, Permutation, Parametric and Bootstrap Tests of Hypotheses, Springer, New York, 2007.
  • E. D. Kolaczyk, Statistical Analysis of Network Data, Springer, 2009.
  • N. Cressie. Statistics for Spatial data. John Wiley & Sons, New York, 1993.

Note Sulla Modalità di valutazione

The exam consists of the oral presentation of the team project.


Bibliografia
Risorsa bibliografica facoltativaV. Pawlowsky-Glahn and A. Buccianti, Compositional data analysis. Theory and applications. , Editore: John Wiley and Sons Ltd., Anno edizione: 2011
Risorsa bibliografica facoltativaK. V. Mardia and P. Jupp, Directional Statistics , Editore: John Wiley and Sons Ltd., Anno edizione: 2000
Risorsa bibliografica facoltativaP. I. Good, Permutation, Parametric and Bootstrap Tests of Hypotheses, Editore: Springer, New York, Anno edizione: 2007
Risorsa bibliografica facoltativaE. D. Kolaczyk, Statistical Analysis of Network Data, Editore: Springer, Anno edizione: 2009
Risorsa bibliografica facoltativaN. Cressie, Statistics for Spatial data, Editore: John Wiley & Sons, New York, Anno edizione: 1993

Mix Forme Didattiche
Tipo Forma Didattica Ore didattiche
lezione
24.0
esercitazione
16.0
laboratorio informatico
0.0
laboratorio sperimentale
0.0
progetto
0.0
laboratorio di progetto
0.0

Informazioni in lingua inglese a supporto dell'internazionalizzazione
Insegnamento erogato in lingua Inglese
Disponibilità di materiale didattico/slides in lingua inglese
Disponibilità di libri di testo/bibliografia in lingua inglese
Possibilità di sostenere l'esame in lingua inglese
Disponibilità di supporto didattico in lingua inglese
schedaincarico v. 1.6.1 / 1.6.1
Area Servizi ICT
08/12/2019