The ABC algorithm is a Bayesian method which simulates samples from the posterior distribution. In this thesis, the method is applied on both synthetic and observed data of a regression model. Under normal error distribution a conjugate prior and the likelihood function are used in the algorithm. Additionally, a bootstrap method is implemented in a modified algorithm to provide an alternative method, without requiring normal error distribution. The results of both methods are thereafter presented and compared with the analytic posterior under a conjugate prior, to evaluate their performances. Lastly, advantages and possible issues are discussed.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:uu-504657 |
| Date | January 2023 |
| Creators | Nyman, Ellinor |
| Publisher | Uppsala universitet, Statistik, AI och data science |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
| Relation | U.U.D.M. project report ; 2023:6 |
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