Unit STATISTICAL COMPUTING METHODS
- Course
- Finance and quantitative methods for economics
- Study-unit Code
- A000208
- Location
- PERUGIA
- Curriculum
- Statistical data science for finance and economics
- Teacher
- Francesco Bartolucci
- CFU
- 12
- Course Regulation
- Coorte 2022
- Offered
- 2023/24
- Type of study-unit
- Obbligatorio (Required)
- Type of learning activities
- Attività formativa integrata
MOD. I STATISTICAL COMPUTING
| Code | A000209 |
|---|---|
| Location | PERUGIA |
| CFU | 6 |
| Teacher | Silvia Pandolfi |
| Teachers |
|
| Hours |
|
| Learning activities | Affine/integrativa |
| Area | Attività formative affini o integrative |
| Academic discipline | SECS-S/01 |
| Type of study-unit | Obbligatorio (Required) |
| Language of instruction | English |
| Contents | Numerical and advanced statistical methods will be presented starting from real case studies and analyzed using the R language. |
| Reference texts | Braun, W.J., and Murdoch, D.J. (2007). A First Course in Statistical Programming with R, Cambridge University Press. Jones, O., Maillardet, R. and Robinson, A. (2009). Introduction to Scientific Programming and Simulation Using R, Chapman & Hall/CRC. Rizzo, M. L. (2008). Statistical Computing with R, Chapman & Hall/CRC. Voss, J. (2013). An Introduction to Statistical Computing, John Wiley & Sons. McLachlan, G. and Peel. D. (2004). Finite Mixture Models, John Wiley & Sons. Other materials made available to students attending classes. |
| Educational objectives | After completing the course, the student will be able to implement and apply appropriate numerical methods and statistical tools to real problems with the R software. |
| Prerequisites | The course introduces advanced topics in statistical computing. Prior knowledge of the fundamental concepts of statistics and probability will be assumed. In addition, a basic knowledge of the R software is required for laboratory activities. |
| Teaching methods | Lectures and lab sessions with the R software. |
| Other information | For information about services for students with disabilities and /or DSA visit the page http://www.unipg.it/disabilita-e-dsa |
| Learning verification modality | Take-home written exams to be done in R. Final oral exam about the topics of the course. The lab sessions are aimed at assessing the student's ability in implementing the methodologies introduced during the course. The final exam is aimed at assessing the level of knowledge reached by the student with respect to computational and methodological aspects covered by the course. |
| Extended program | The course introduces numerical methods and advanced topics in statistical computation that are used in many fields, such as finance and economy, data mining, and social sciences. Real case studies will be analyzed using the R software. A selection of topics included is: - Monte Carlo Simulations - Monte Carlo Integration - Numerical optimization techniques - Latent variable models: finite mixture models, latent class models, hidden Markov models - EM Algorithm - Bootstrap Inference |
MOD. II BAYESIAN COMPUTING
| Code | A000210 |
|---|---|
| Location | PERUGIA |
| CFU | 6 |
| Teacher | Francesco Bartolucci |
| Teachers |
|
| Hours |
|
| Learning activities | Affine/integrativa |
| Area | Attività formative affini o integrative |
| Academic discipline | SECS-S/01 |
| Type of study-unit | Obbligatorio (Required) |
| Language of instruction | English |