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Bayesova statistika v oblaku
ID Hercog, Uroš (Author), ID Štrumbelj, Erik (Mentor) More about this mentor... This link opens in a new window, ID Pančur, Matjaž (Co-mentor)

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Abstract
Zaradi vse hitrejšega razvoja in dostopnosti zmogljive strojne opreme je računalništvo v oblaku postalo zanimivo na področju dela s podatki, strojnega učenja in statistike. Izvajanje učenja modelov in obdelave veliko podatkov želijo uporabniki premakniti v visoko-stopnjevalne oblake. Tam te procese izvedejo z višjo stopnjo vzporednega izvajanja, kot jo lahko dosežejo na osebnih računalnikih. Vendar pa so uporabniki omejeni pri uporabi orodij, saj vsa oddaljenega izvajanja ne podpirajo. Takšno orodje je tudi Stan, za katerega smo v nalogi razvili rešitev v oblaku. Pregledali smo področje orodij statističnega modeliranja, poiskali sorodne rešitve in obstoječe rešitve, ki omogočajo uporabo orodja Stan v oblaku. Zbrali smo funkcionalne zahteve in razdelali arhitekturo platforme za Stan v oblaku. Na podlagi predlagane arhitekture smo razvili rešitev, imenovano Cloudstan. Platforma je sestavljena iz zalednega dela, ki skrbi za orkestracijo izvajanja prevajanja in vzorčenja modelov Stan, orodje ukazne vrstice in knjižnico v programskem jeziku R, ki omogočata komunikacijo z zalednim sistemom.

Language:Slovenian
Keywords:SaaS, Stan, Bayesova statistika, računalništvo v oblaku
Work type:Master's thesis/paper
Typology:2.09 - Master's Thesis
Organization:FRI - Faculty of Computer and Information Science
Year:2021
PID:20.500.12556/RUL-132817 This link opens in a new window
COBISS.SI-ID:84027139 This link opens in a new window
Publication date in RUL:04.11.2021
Views:1212
Downloads:64
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Secondary language

Language:English
Title:Bayesian statistics in the cloud
Abstract:
The proliferation and accessibility of high-performance hardware made cloud computing interesting in the areas of data processing, machine learning and statistics. Users are moving the model training and processing of data to scalable cloud solutions which allow them to execute these processes in a highly parallel manner. This allows them to complete their tasks in less time than on personal computers. But not all tools used by experts have native support for remote execution. In this master’s thesis, we developed a cloud solution for a tool for statistical modeling called Stan. We analyzed and compared cloud solutions for tools similar to Stan. We collected functional requirements and presented the system architecture. Based on the architecture, we developed the platform called Cloudstan, a command-line interface and a library for communicating with the platform written in R.

Keywords:SaaS, Stan, Bayessian statistics, cloud computing

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