Créditos
6
Tipos
Optativa
Requisitos
Esta asignatura no tiene requisitos
, pero tiene capacidades previas
Departamento
AC
Horas semanales
Teoría
4
Problemas
0
Laboratorio
0
Aprendizaje dirigido
0
Aprendizaje autónomo
0
Objetivos
-
The main goal of the course is to develop in the students quantitative modeling skills, based on probabilistic techniques.
Competencias relacionadas:
Contenidos
-
Discrete probability models
Basic results. Examples: IQ switch max throughput, hash tables and ethernet switching. Anticolision methods in RFID tags. Blocking probabilities in optical switches. TCP window model. Bayesian antispam filters. Fountain codes. -
Continuous probability models
Basic results. Exponential and Poisson distribution. Palm's theorem. PASTA. Residual times paradox. Large number laws. Normal distribution and Central Limit theorem. Multivariate Gaussian distributions. Examples: Basic teletraffic models. Path estability in MANETs. Epidemic models in networks. Additive Gaussian Noise. Filtering smartphone sensor data. -
Lineal systems and signal space
Lineal spaces and lineal systems. Orthogonality. Fourier Series. Sampling theorem. Fast Fourier Transform. Random processes. Examples: Wireless transmission. IEEE 802.11g and 802.11n. Image compression.
Actividades
Actividad Acto evaluativo
Basic results of discrete probability
Teoría
6h
Problemas
0h
Laboratorio
0h
Aprendizaje dirigido
0h
Aprendizaje autónomo
0h
Examples of discrete probability models
Teoría
9h
Problemas
0h
Laboratorio
0h
Aprendizaje dirigido
0h
Aprendizaje autónomo
0h
Basic results on continuous probability
Teoría
9h
Problemas
0h
Laboratorio
0h
Aprendizaje dirigido
0h
Aprendizaje autónomo
0h
Examples of continuous-probability models
Teoría
9h
Problemas
0h
Laboratorio
0h
Aprendizaje dirigido
0h
Aprendizaje autónomo
0h
Lineal systems and signal space
Teoría
12h
Problemas
0h
Laboratorio
0h
Aprendizaje dirigido
0h
Aprendizaje autónomo
0h
Lineal system and signal space examples
Teoría
9h
Problemas
0h
Laboratorio
0h
Aprendizaje dirigido
0h
Aprendizaje autónomo
0h
Metodología docente
During the initial sessions of each theme, the main results will be explained in the blackboard. During the other sessions, will discuss in the classroom performance models taken from research papers.Método de evaluación
The evaluation is based on three different activities- Short presentations of research papers (P)
- A detailed study of one paper (D)
- A final exam (E)
Each of the three activities will be evaluated (0=<mark=<10).
The final mark for the course (F) will be:
F= 0.25xP+0.25xD+0.5xE
Bibliografía
Básico
-
Information theory, inference, and learning algorithms
- Mackay, David,
Cambridge University Press,
cop. 2003.
ISBN: 9780521642989
http://cataleg.upc.edu/record=b1269009~S1*cat -
An Introduction to probability theory and its applications (vol 1 and 2)
- Feller, William,
John Wiley and Sons,
cop. 1968.
ISBN: 0471257087
http://cataleg.upc.edu/record=b1005063~S1*cat -
Linear Mathematics in Infinite Dimensions: Signals, Boundary Value Problems and Special Functions
- U. H. Gerlach ,
May 2010 .
http://www.math.osu.edu/~gerlach.1/math/BVtypset/
Web links
- You can find a pdf copy of MacKay's book http://www.inference.phy.cam.ac.uk/mackay/itila/