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This course is offered through the Network for Inverse Problems. These studies are available for the following degree students:

","fi":"

Tämä opintojakso on tarjolla Inversio-ongelmien verkostossa. Verkoston opinnot ovat tarjolla seuraavien tutkinto-ohjelmien opiskelijoille:

"},"cooperationNetwork":{"abbreviation":"inversio","name":{"en":"Education network on Inverse Problems","fi":"Inversio-ongelmien koulutusverkosto"}}}],"gradeScaleId":"sis-0-5","outcomes":{"en":"After the course, the student - understands multivariate probability densities and the related parameters - understands the principles of different estimation approaches - is able to derive different types of linear estimators, depending on various different assumptions, for example: ML-estimator, MS-estimator, MAP-estimator, LS-estimator - understands the concept of nonlinear estimation The course develops the following generic skills: critical thinking, identification and development of one's own expertise, interaction and communication. - understands the idea of time-varying (dynamic) estimation - knows the Kalman filter approach in dynamic estimation - is able to write computer code for different types of estimation approaches - can apply the estimators in practical estimation problems","fi":"Opintojakson suoritettuaan opiskelija - hallitsee usean muuttujan todennäköisyysjakaumien käsitteet - ymmärtää eri estimointitapojen periaatteet ja lähtökohdat - osaa johtaa erilaiset lineaariset estimaattorit oletuksista riippuen, esimerkiksi: ML-estimaatti, MS-estimaatti, MAP-estimaatti, LS-estimaatti - tuntee epälineaarisen estimoinnin käsitteen - ymmärtää aikariippuvan estimaatin käsitteen - tuntee Kalman suotimeen perustuvan dynaamisen estimoinnin - osaa ohjelmoida erilaisia käytännön estimaattoreita tietokoneella - osaa soveltaa estimaattoreita käytännön estimointitilanteisiin Opintojakso kehittää seuraavia geneerisiä taitoja: kriittinen ajattelu, oman asiantuntijuuden tunnistaminen ja kehittäminen, vuorovaikutus ja viestintä."},"tweetText":null,"content":{"en":"Probability theory, Bayes estimation, Mean square estimation, Maximum a posteriori estimation, Gauss-Markov estimation, Least squares estimation, time-varying estimation, non-linear estimation, examples.","fi":"Todennäköisyyslaskentaa, Bayes estimointi, MS-estimointi, Maximum a Posteriori estimointi, Gauss-Markov estimointi, LS-estimointi, aikariippuva estimointi, epälineaarinen estimointi, esimerkkejä."},"additional":{"en":"The course will be held only every second academic year. You can see the course implementations in Study guide, under “Course Units” or “Show past courses”. This course is intended for the following student groups: - Undergraduate students in Technical Physics (MSc) - Undergraduate students in Applied Physics (MSc)","fi":"Kurssi luennoidaan joka toinen vuosi. Tarkista seuraavan toteutuksen ajankohta opinto-oppaasta kohdasta ”Toteutukset” tai ”Näytä menneet toteutukset”. Tämä opintojakso on tarkoitettu seuraaville opiskelijaryhmille: - Teknillisen fysiikan tutkinto-opiskelijat (diplomi-insinööri) - Sovelletun fysiikan tutkinto-opiskelijat (filosofian maisteri)"},"prerequisites":null,"compulsoryFormalPrerequisites":[],"recommendedFormalPrerequisites":[],"literature":[],"learningMaterial":null,"completionMethods":[]}],"prerequisiteCourseUnit":[],"prerequisiteModule":[]},"prerequisiteCourseUnitPage":{"nodes":[]},"prerequisiteModulePage":{"nodes":[]},"parentModulePage":{"nodes":[]}},"pageContext":{"type":"courseUnit","locale":"en","title":"Estimation Theory","id":"otm-78d6d075-fa03-3507-9090-3777b11049a2","code":"LS00EN30","prerequisiteCourseUnitIds":[],"prerequisiteModuleIds":[],"parentModuleIds":[],"curriculumPeriodStartDate":"2026-08-01","curriculumPeriodEndDate":"2027-08-01","coordinatingOrgIds":[],"searchable":false,"searchTags":null,"organisationIds":["otm-42edd1ec-1dc5-35c1-b11f-d74a959e96c9"],"organisations":[],"attainmentLanguages":["en"],"hasSummerStudies":false,"teachingPeriods":[],"cooperationNetworkDirection":"INBOUND","hasCooperationNetworkSettings":true,"hasAvoinTeaching":false}}}