Browsing NTNU Open by Author "Taraldsen, Gunnar"
Now showing items 1-20 of 21
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Autoregressive Normalising Flows for Density Estimation and Variational Inference: A proper introduction and a novel flow
Hultén, Edvard (Master thesis, 2020)I denne oppgaven presenterer vi en klasse med modeller kalt "normalising flows". Dette er en klasse med modeller som drar nytte av fleksibiliteten og de beregningsmessige fordelene som tilbys av det moderne dyp lærings-paradigmet, ... -
Conditional fiducial models
Taraldsen, Gunnar; Lindqvist, Bo Henry (Journal article; Peer reviewed, 2017)The fiducial is not unique in general, but we prove that in a restricted class of models it is uniquely determined by the sampling distribution of the data. It depends in particular not on the choice of a data generating ... -
Conditional Monte Carlo revisited
Lindqvist, Bo Henry; Erlemann, Rasmus; Taraldsen, Gunnar (Peer reviewed; Journal article, 2021)Conditional Monte Carlo refers to sampling from the conditional distribution of a random vector X given the value T ( X ) = t for a function T ( X ) . Classical conditional Monte Carlo methods were designed for estimating ... -
The Confidence Density for Correlation
Taraldsen, Gunnar (Peer reviewed; Journal article, 2021)Inference for correlation is central in statistics. From a Bayesian viewpoint, the final most complete outcome of inference for the correlation is the posterior distribution. An explicit formula for the posterior density ... -
Coulombgass i To Dimensjoner
Taraldsen, Gunnar (Master thesis, 1987) -
Discussion of ‘Nonparametric generalized fiducial inference for survival functions under censoring’
Taraldsen, Gunnar; Lindqvist, Bo Henry (Journal article; Peer reviewed, 2019)The following discussion is inspired by the paper Nonparametric generalized fiducial inference for survival functions under censoring by Cui and Hannig. The discussion consists of comments on the results, but also indicates ... -
Disentangled Representations in Variational Autoencoders
Mushom, Lars (Master thesis, 2020)Dype generative modeller omfatter modeller som kombinerer ideer fra sannsynlighetsteori med fleksible dype nevrale nettverk som skalerer til høy-dimensjonale data. Å lære generative modeller som fanger en representasjon ... -
Fiducial Inference and Decision Theory
Taraldsen, Gunnar; Lindqvist, Bo Henry (Chapter, 2024) -
Grunnkurs i statistikk og sannsynlighetsteori
Taraldsen, Gunnar (Book, 1997) -
Improper priors and improper posteriors
Taraldsen, Gunnar; Tufto, Jarle; Lindqvist, Bo Henry (Peer reviewed; Journal article, 2021)What is a good prior? Actual prior knowledge should be used, but for complex models this is often not easily available. The knowledge can be in the form of symmetry assumptions, and then the choice will typically be an ... -
Measurability of intersections of measurable multifunctions
Taraldsen, Gunnar (Journal article, 1996)We prove universal compact-measurability of the intersection of a compact-measurable Souslin family of closed-valued multifunctions. This generalizes previous result on intersections of measurable multifunctions. We introduce ... -
Objective Beliefs and Bayes Estimators: An Approach to Parameter Estimation Using Objective Priors and Distance Between Distributions
Sæternes, Erik Hide (Master thesis, 2020)Denne oppgaven tar for seg problemet med å estimere parametre i statistisk analyse, og i særdeleshet Bayesiansk analyse. Gitt en modell utledes objektive a priori-fordelinger for parametrene. De resulterende fordelingene ... -
Objective inference for correlation
Helland-Moe, Olav (Master thesis, 2021)Denne masteroppgaven tar for seg problemstillingen om å hente mest mulig informasjon om korrelasjonen i en binormal fordeling basert på observerte punkter i planet. Forventningsverdiene og variansene er antatt kjente. Til ... -
On the proper treatment of improper distributions
Lindqvist, Bo Henry; Taraldsen, Gunnar (Journal article; Peer reviewed, 2017)The axiomatic foundation of probability theory presented by Kolmogorov has been the basis of modern theory for probability and statistics. In certain applications it is, however, necessary or convenient to allow improper ... -
Predictors of adherence and the role of primary non-adherence in antihormonal treatment of breast cancer
Dragvoll, Ida; Bofin, Anna M.; Søiland, Håvard; Taraldsen, Gunnar; Engstrøm, Monica J (Peer reviewed; Journal article, 2022)Background Antihormonal treatment for hormone receptor (HR) positive breast cancer has highly beneficial effects on both recurrence rates and survival. We investigate adherence and persistence in this group of ... -
Quantifying Predictive Uncertainty in Artificial Neural Networks
Lehre, Christian Nilsen (Master thesis, 2021)To metoder for å konstruere Bayesianske nevrale nettverk, MC Dropout og SGVB, er implementert og anvendt på et reelt datasett levert av det norske E\&P selskapet Aker BP. Datasettet består av brønndata hentet fra 34 brønner ... -
Simulated Exact Confidence Intervals: With Applications to Censored Exponential Reliability Data
Lindqvist, Bo Henry; Taraldsen, Gunnar (Journal article; Peer reviewed, 2024)A method for constructing exact simulated confidence intervals is presented, valid for situations with both discrete and continuous observations. The idea of the method is to invert a data generating function, which needs ... -
Spectral theory of random operators: The energy spectrum of the quantum electron in a disordered solid
Taraldsen, Gunnar (Dr. ingeniøravhandling, 0809-103X; 1992:89, Doctoral thesis, 1992) -
Speech Enhancement with a Generative Adversarial Network
Vik, Mira Lilleholt (Master thesis, 2019)Hvem har ikke vært i en samtale forvrengt av bakgrunnslyd som trafikk eller vind? En algoritme som kan forbedre et støyete talesignal er av interesse i mange hverdagslige situasjoner. Vi har implementert en deep learning ... -
Statistical Machine Learning on Covid-19 Time Series using Econometrics
Brataas, Eivind Hagemann (Master thesis, 2022)I denne oppgaven blir tre tidsrekkemodeller sin evne til å predikere framtidige nye tilfeller av Covid-19 sammenliknet. Den første modellen er en maskinlæringsmodell av typen CNN-LSTM. Da modellen ble publisert i 2021, var ...