. "Other Statistics (rather Than Geostatistics) Kas"@en . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . "Mathematical and statistical techniques"@en . . "6.0" . "Learning objectives\n\n \n\nReferring to knowledge\n\n\nGet acquainted with fundamental results of probability theory and statistics. Understand their relevance to important issues in experimental and theoretical physics.\nUnderstand the power and limitations of Monte-Carlo methods, in particular when applied to physical contexts\nDevelop comprehensive skills on the topic, ranging from the ability to write code to perform computations on specific data to the ability to prove easy mathematical statements in order to solve theoretical issues.\nGet acquainted with the techniques for data analysis and the basic concepts of data mining. Specifically, to code in Python to implement the analysis and to use a variety of software tools for data mining, including Neural Networks.\n \n\n \n\nTeaching blocks\n\n \n\n1. The concept of probability\n1.1. Conditional probability and Bayes theorem\n\n1.2. Frequentists versus Bayesians\n\n2. Random variables\n2.1. Mean, variance and moments\n\n2.2. Change of variables\n\n2.3. Examples of one-dimensional p.d.f’s\n\n2.4. Distributions of more than one random variable\n\n2.5. Examples of n-dimensional p.d.f’s\n\n2.6. Reproducibility\n\n2.7. Some theorems of probability theory\n\n3. Monte Carlo\n3.1. Random generation of uniform numbers\n\n3.2. Generation of different p.d.f.\n\n3.3. The inverse transformation method\n\n3.4. The composition method\n\n3.5. Von Neumann’s method\n\n3.6. Stratified sampling method\n\n3.7. Events with weight\n\n3.8. Monte Carlo integration\n\n3.9. Markov chains\n\n4. Statistical inference\n4.1. Non-parametric estimation\n\n4.2. Parametric estimation\n\n4.3. Confidence intervals\n\n4.4. Fisher Information\n\n4.5. Sufficient statistics\n\n4.6. Cramer-Rao inequality\n\n4.7. Construction of estimators\n\n4.8. The maximum likelihood method\n\n4.9. The minimum chi2 method\n\n5. Statistical tests\n5.1. Hypothesis test\n\n5.2. Significance test\n\n5.3. Decision theory\n\n6. Advanced topics\n6.1. Feldman-Cousins criterion for confidence intervals\n\n6.2. The sPlot method\n\n6.3. The sFit method\n\n7. Multivariate analysis and statistical treatment techniques\n7.1. Introduction to multivariate data analysis\n\n7.2. Data analysis and representation; Statistical distances.\n\n7.3. Principal component analysis\n\n7.4. Clustering\n\n7.5. Discriminant analysis\n\n7.6. Non-parametric methods of estimation of a probability density function\n\n7.7. Hands-on exercises\n\n8. Neural Networks\n8.1. Basic concepts of Artificial Neural Networks\n\n8.2. Design, training and use of Neural Networks\n\n8.3. Self Organizing maps\n\n8.4. Hands-on exercises\n\n9. Data mining\n9.1. Introduction to data mining: basic concepts\n\n9.2. Combination of data analysis techniques to implement a data mining procedure\n\n9.3. Complementary topics: Big data, artificial intelligence, cloud computing\n\n \n\n \n\nOfficial assessment of learning outcomes\n\n \n\nThere is no exam for this subject. Instead, 6 problem-solving assignments are set during the course. Grading is based on the assessment of the reports submitted.\n\n \n\n \n\nExamination-based assessment\n\nRepeat assessment: students have to repeat and resubmit the 6 problem-solving assignments following the instructions from the lecturers. Once the assignments have been assessed, students take an oral exam on their contents. If this exam is successfully passed, the final grade is calculated from the marks of the assignments; otherwise, the subject is graded as failed.\n\n \n\n \n\n \n\nReading and study resources\n\nCheck availability in Cercabib\n\nBook\n\nDeGroot, Morris H. Probability and statistics. 4th ed. Boston : Pearson Education, cop. 2012 Enllaç\n\n2a ed Enllaç\n\nFeller, William. An introduction to probability theory and Its applications, 2nd ed. New York : Wiley, 1972. v. 2 Enllaç\n\n\nhttps://cercabib.ub.edu/discovery/search?vid=34CSUC_UB:VU1&search_scope=MyInst_and_CI&query=any,contains,b1536375* Enllaç\n\nWitten, I. H. ; Frank, Eibe ; Hall, Mark A. Data mining : a practical machine learning tools. 4th ed. Burlington, [etc.] : Morgan Kaufman, cop. 2017 Enllaç\n\n\nhttps://cercabib.ub.edu/discovery/search?vid=34CSUC_UB:VU1&search_scope=MyInst_and_CI&query=any,contains,b1727639* Enllaç\n\nLandau, David P ; Binder, K. A guide to Monte Carlo simulations in statistical physics. 4a ed. Cambridge : Cambridge University Press, cop. 2015 Enllaç\n\n\nData Mining: Practical Machine Learning Tools and Techniques; Ian H. , Witten, Eibe Frank, Mark A. Hall, Christopher Pal; Ed. Morgan Kauffmann, ISBN 978-0128042915\n\n\nVideo, DVD and film\n\nNeural Networks: Zero to Hero: youtube series on Neural Networks\n\nhttps://www.youtube.com/playlist?list=PLAqhIrjkxbuWI23v9cThsA9GvCAUhRvKZ Enllaç\n\nArticle\n\nWeinzierl, Stephan. \"Introduction to Monte Carlo method\", a: http://arxiv.org/abs/hep-ph/0006269 Enllaç\n\n \tConferences\n\nWeb page\n\nScientific computing tools for Python: https://www.scipy.org/about.html \n\nIntroduction to Probability for Data Science: https://probability4datascience.com/\n\nMore information at: http://grad.ub.edu/grad3/plae/AccesInformePDInfes?curs=2023&assig=568423&ens=M0D0B&recurs=pladocent&n2=1&idioma=ENG" . . "Presential"@en . "TRUE" . . "Master in Astrophysics, Particle Physics and Cosmology"@en . . "https://web.ub.edu/en/web/estudis/w/masteruniversitari-m0d0b" . "60"^^ . "Presential"@en . "The master's degree Astrophysics, Particle Physics and Cosmology of the University of Barcelona is intended for holders of bachelor's degrees and equivalent undergraduate degrees (particularly in physics), engineers and technical engineers who wish to pursue a specialization in one of the following branches of knowledge: astrophysics and space sciences; atomic, nuclear and particle physics; or gravitation and cosmology. The duration and specific content will depend on each applicant's previous studies.\nThe master's degree seeks to provide students with the training needed to conduct research in one of the fields listed above or in a related field, thanks to the interdisciplinary subjects also included in the program.\n\nThe course focuses on preparing students to begin a doctoral thesis upon completion of their degree, enabling them to pursue an academic career. However, it also provides highly valuable training for a career in the public or private sector, opening up a wide range of employment options.\n\nObjectives\nThe objectives of the master's degree are to provide students with advanced academic training in the fields of astrophysics, space sciences, atomic, nuclear and particle physics, gravitation and cosmology. More specifically, the objectives are:\n\n\n\nto study the content of a carefully selected set of subjects;\n\nto acquire the work methodology needed for conducting research and completing a doctoral thesis in the above fields through the completion of one or more research projects during the program;\n\nto acquire the skills needed to give scientific presentations;\n\nto acquire the competences, skills and abilities required to join a research group and complete doctoral studies or eventually join companies that pursue developments related to research in the mentioned fields.\n\nCompetences\nThe generic competences obtained by students will be instrumental (such as the capacity for analysis and synthesis, a working knowledge of English, knowledge of software tools and decision-making skills), interpersonal (such as critical reasoning, teamwork and creativity), and systemic (such as the capacity for independent learning and the capacity to adapt to new situations).\n\nThe specific competences obtained by students will be the capacity to understand a physical system in terms of the relevant scales of energy, the capacity to identify observable magnitudes and the capacity to test predictions from theoretical models with experimental and observational data.\n\nAnother potential specific competence is the capacity to develop and apply new technologies."@en . . . "1"@en . "FALSE" . . . "Master"@en . "Thesis" . "1660.20" . "Euro"@en . "4920" . "None" . "Obtaining the Master's Degree in Astrophysics, Particle Physics and Cosmology is the first step towards undertaking a doctoral thesis in one of the research lines in the general fields of Astronomy and Astrophysics (astrophysics and space sciences) or Particle Physics and Gravitation (atomic, nuclear and particle physics, gravitation and cosmology). Some of the more applied syllabus content may also open professional doors to work in companies in the aerospace, energy, financial and communications sectors, among others, as these require specialists in the fields of space science, data processing and analysis, process simulation and advanced computation, etc."@en . "2"^^ . "TRUE" . "Upstream"@en . . . . . . . . . . . . . . . . . . . . .