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Matematisk statistik Lösningar: 2024–08–30 kl 0800–1300 Matematikcentrum FMSF40 Lunds universitet Sannolikhetsteori och diskret matematik 1. Givet att ξ ∈ N(m, 0.1) vill vi bestäma µ s̊a att P (ξ ≥ 5) = 0.99 P ( ξ −m 0.1 ≥ 5−m 0.1 ) = 0.99 Vi söker allts̊a ett värde s̊a att en N(0,1) fördelning är större än värdet i 99% av fallen. Detta svarar mot −λ0.01 = −2.3263, vilket ger 5−m 0.1 = −λ
Matematisk statistik Tentamen: 2024–11–01 kl 0800–1300 Matematikcentrum FMSF40 Lunds universitet Sannolikhetsteori och diskret matematik Lösningsförslag 1. Kretskort: Definiera händelserna T: tillverkad p̊a Taiwan, K: tillverkad i Kalifornien samt D: defekt enhet. Vi har d̊a ur uppgiften P (D | T ) = 0.001 och P (D | K) = 0.03 samt att T ∪K = Ω eller att K = T c. (a) Vi f̊ar att P (K) = 0.2 och
Matematik LTH Helsingborg Tentamensskrivning, FMSF40 Sannolikhetsteori och diskret matematik 2023-10-27 kl 8.00–13.00 • Hjälpmedel: Miniräknare och utdelad formelsamling. • Lösningar ska vara försedda med ordentliga motiveringar och svaren förenklas maxi- malt. • Skriv anonymkod (eller namn om du saknar kod) p̊a varje papper. • P̊a omslaget m̊aste du skriva med bläck. • Skriv endast p̊a ena
Matematik LTH Helsingborg Tentamensskrivning, FMSF40 Sannolikhetsteori och diskret matematik 2024-04-03 kl 8.00–13.00 • Hjälpmedel: Miniräknare och utdelad formelsamling. • Lösningar ska vara försedda med ordentliga motiveringar och svaren förenklas maxi- malt. • Skriv anonymkod (eller namn om du saknar kod) p̊a varje papper. • P̊a omslaget m̊aste du skriva med bläck. • Skriv endast p̊a ena
Matematisk statistik Tentamen: 2024–08–30 kl 0800–1300 Matematikcentrum FMSF40 Lunds universitet Sannolikhetsteori och diskret matematik • Hjälpmedel: Miniräknare och utdelad formelsamling • Lösningar ska vara försedda med ordentliga motiveringar och svaren förenklas maximalt • Skriv anonymkod (eller namn om du saknar kod) p̊a varje papper • P̊a omslaget m̊aste du skriva med bläck • Skriv en
Microsoft Word - PMexa_kand[1] English NT version 2.docx Cent re fo r Mathemat ica l Sc iences Mathemat ics , Facu l ty o f Sc ience Degree projects for a Bachelor’s degree in Mathematics The Bachelor’s degree programme concludes with a degree project that consists of an independent assignment selected in consultation with a supervisor. It can be a minor mathematical research task,
https://www.maths.lu.se/fileadmin/maths/Matematik_NF/Kandidatprogram/PMexa_kand_1__English_NT_version_2.pdf - 2025-07-03
Microsoft Word - NAMAT-NA-Courses-Eng.docx Appendix to Programme Syllabus established by the Board of the Faculty of Science on 2007-02-07. The Course requirements have been approved by the Study Programmes Board on 2012-03-29, with latest update on 2016-12-08. COURSE REQUIREMENTS FOR A GENERAL QUALIFICATION Degree of Master of Science 120 credits Major: Mathematics With specialization in Numerica
https://www.maths.lu.se/fileadmin/maths/Matematik_NF/Masterprogram/NAMAT-NA-Courses-Eng.pdf - 2025-07-03
CLOSING THE GAP BETWEEN SCHOLARSHIP AND PRACTICE TRACY S. CRAIG A great deal of research has been carried out on the teaching and learning of subspaces, linear independence, basis, span – concepts students struggle to understand. No clear route for that research to influence classroom practice. 05/11/22Øresundsdagen 3 - Lund, Sweden 2 November 2022 2 THE EXISTENCE OF A GAP LINEAR ALGEBRA AS AN EXA
https://www.maths.lu.se/fileadmin/maths/Oresundsdagen_3/craig.pdf - 2025-07-03
Group assignments in an online mathematics course Laura Fainsilber, Linnea Hietala Chalmers, Göteborgs universitet Group assignments in an online course for the Foundation Year (Tekniskt Basår) Goals: • social contact • bridging the gap between secondary school and university math • developing forms for group work • mathematical communication • learning to handle open-ended questions • using GeoGe
https://www.maths.lu.se/fileadmin/maths/Oresundsdagen_3/fainsilber_etal.pdf - 2025-07-03
Emne: An overview of the paper The challenges of basing a full mathematics course entirely on realistic examples about a two-year struggle with the development of a first-term course containing several new (or at least: rare) pedagogical elements. Focus will be on the use made of results obtained in pedagogical research carried out prior to, as well as along with, the writing process and the actua
https://www.maths.lu.se/fileadmin/maths/Oresundsdagen_3/ottosen.pdf - 2025-07-03
Syllabus Mathematics Page 1 of 12 International Office, LTH Reg. No. U 2020/679 Date 2020-09-08 General syllabus for third-cycle studies in Mathematics TEFMAF00 The syllabus was approved by the Board of the Faculty of Engineering/LTH 21 May 2008 and most recently amended 8 September 2020 (reg. no U 2020/679). 1. Subject description Mathematics is a science that uses logical inferences to study con
https://www.maths.lu.se/fileadmin/maths/forskarutbildning/Studieplaner/Matematik_en.pdf - 2025-07-03
Syllabus Mathematical Statistics Page 1 of 11 International Office, LTH Reg. No. U 2020/679 Date 2020-09-08 General syllabus for third-cycle studies in Mathematical Statistics TEFMSF00 The syllabus was approved by the Board of the Faculty of Engineering/LTH 24 September 2007 and most recently amended 8 September 2020 (reg. no U 2020/679). 1. Subject description Mathematical statistics encompasses
https://www.maths.lu.se/fileadmin/maths/forskarutbildning/Studieplaner/Matematisk_statistik_en.pdf - 2025-07-03
Dynamical Borel-–Cantelli Lemmas Alejandro Sponheimer 21/03/2023 The classical Borel-Cantelli lemma in probability theory concerns sequences of independent events and whether or not infinitely many of the events occur. In the context of dynamical systems though, one rarely works with independent sets. Nevertheless, it is sometimes possible to prove dynamical versions of the Borel-Cantelli lemma. I
https://www.maths.lu.se/fileadmin/maths/personal_staff/PhD_seminar/alejandrosponheimer-phdseminar.pdf - 2025-07-03
Time difference of arrival estimation using neural networks Axel Berg 05/05/2022 In this talk I will go through the basics of time difference of arrival (TDOA) estimation using pairs of microphones, which can be used for acoustic sound source localization. Poor TDOA estimation is a common problem in adverse conditions where interference from noise and reverberation dominate the re- ceived signals.
https://www.maths.lu.se/fileadmin/maths/personal_staff/PhD_seminar/axelberg-phdseminar.pdf - 2025-07-03
Fantastic inliers and where to find them Erik Tegler 06/12/2022 A brief introduction to some of the ideas behind the field of robust esti- mation. We will mainly discuss the ”Random sample consensus”-algorithm (RANSAC), which enables us to solve non-convex optimization problems by in- stead solving many subproblems. This will culminate with some examples of applications in computer vision. 1
https://www.maths.lu.se/fileadmin/maths/personal_staff/PhD_seminar/eriktegler-phdseminar.pdf - 2025-07-03
Synchronization in the Kuramoto model Felix Augustsson 19/05/2022 One interesting class of ODE:s related to topics such as the brain, fireflies and people walking on bridges are networks of oscillators. Such networks exhibit interesting non-transient behaviors. However, the systems can generally not be solved exactly due to their complexity and size. In this talk, some classic results concerning t
https://www.maths.lu.se/fileadmin/maths/personal_staff/PhD_seminar/felixaugustsson-phdseminar.pdf - 2025-07-03
Representations of Quivers and Normalform Problems for Matrices Jan Thomm 08/11/2022 We give a short introduction to quivers and their representation theory. With this, we aim to "classify" certain configurations of matrices. In particular, we will see that it is extremely hard to classify two endomorphisms of a vector space simultaneously. 1
https://www.maths.lu.se/fileadmin/maths/personal_staff/PhD_seminar/janthomm-phdseminar.pdf - 2025-07-03
My mama always said a matrix was like a box of numbers Magnus Fries 22/11/2022 A short introduction to the correspondence between topological spaces (geo- metric things) and commutative C∗-algebras (algebraic things) as well as spec- trum of linear operators (space of eigenvalues). This will culminate in the neat tool of continuous functional calculs with which we can, in a sense, treat any normal
https://www.maths.lu.se/fileadmin/maths/personal_staff/PhD_seminar/magnusfries-phdseminar.pdf - 2025-07-03
Camera Calibration and 2D-3D Mapping for Safer Traffic and Saving Lives and Stuff Martin Ahrnbom 31/03/2022 Data driven traffic safety research has previously been difficult due to a lack of an efficient way of collecting large amounts of accurate, non-biased 3D road user statistics. Computer Vision is a promising approach for solving this problem. This seminar will explore how camera calibration
https://www.maths.lu.se/fileadmin/maths/personal_staff/PhD_seminar/martinahrnbom-phdseminar.pdf - 2025-07-03
Best approximations in different settings Olof Rubin 07/02/2023 In this presentation I will discuss how to best approximate a function using polynomials. The answer to the question of what is “best” is different depending on who you ask. To some it may be the approximation that can be explicitly computed in finite time, but here we will search for the actual best approxima- tion. This is an old su
https://www.maths.lu.se/fileadmin/maths/personal_staff/PhD_seminar/olofrubin-phdseminar.pdf - 2025-07-03
