izpis_h1_title_alt

Problem nemških tankov : delo diplomskega seminarja
ID Pozne, Jaša (Author), ID Bernik, Janez (Mentor) More about this mentor... This link opens in a new window, ID Šega, Gregor (Comentor)

.pdfPDF - Presentation file, Download (940,04 KB)
MD5: F315A1319C6A33D2F067EE8FA5593CCD

Abstract
Diploma pojasnjuje matematičen vidik znamenitega Problema nemških tankov. Po povzetku zgodovinskega ozadja tega problema, se delo loči na dva pristopa analize. Prvi del je frekventistični. V njem sta prikazani izpeljavi formul z znanim in neznanim minimumom. Smiselnost formule z znanim minimumom se potrdimi s pomočjo simulacij. Drugi del pa Bayesov. Tukaj nastopi le izpeljava s pomočjo analize.

Language:Slovenian
Keywords:Problem nemških tankov, frekventistični pristop, formula z znanim minimumom, formula z neznanim minimumom, simulacije, linearna regresija, Bayesov pristop.
Work type:Final seminar paper
Typology:2.11 - Undergraduate Thesis
Organization:FMF - Faculty of Mathematics and Physics
Year:2023
PID:20.500.12556/RUL-148432 This link opens in a new window
UDC:519.2
COBISS.SI-ID:162055171 This link opens in a new window
Publication date in RUL:23.08.2023
Views:373
Downloads:74
Metadata:XML RDF-CHPDL DC-XML DC-RDF
:
Copy citation
Share:Bookmark and Share

Secondary language

Language:English
Title:German tank problem
Abstract:
This thesis explains the mathematical aspect of the famous German Tank Problem. After summarising the historical background of this problem, the thesis divides into two approaches of analysis. The first part is frequentist. It shows the derivations of the formulas with known and unknown minima. The meaningfulness of the formula with a known minimum is confirmed by means of simulations. The second part is Bayesian. Here, only the derivation by analysis is done.

Keywords:German tank problem, frequentist approach, known minimum formula, unknown minimum formula, simulations, linear regression, Bayesian approach.

Similar documents

Similar works from RUL:
Similar works from other Slovenian collections:

Back