Sökning: "maximum likelihood-skattning generativa modeller"

Hittade 2 uppsatser innehållade orden maximum likelihood-skattning generativa modeller.

1. 1. Exploring Normalizing Flow Modifications for Improved Model Expressivity

   Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Marcel Juschak; [2023]
   Nyckelord :Normalizing Flows; Motion Synthesis; Invertible Neural Networks; Glow; MoGlow; Maximum Likelihood Estimation; Generative models; normaliserande flöden; rörelsesyntes; inverterbara neurala nätverk; Glow; MoGlow; maximum likelihood-skattning generativa modeller;

   Sammanfattning : Normalizing flows represent a class of generative models that exhibit a number of attractive properties, but do not always achieve state-of-the-art performance when it comes to perceived naturalness of generated samples. To improve the quality of generated samples, this thesis examines methods to enhance the expressivity of discrete-time normalizing flow models and thus their ability to capture different aspects of the data. LÄS MER

3. 2. Rating corrumption within insurance companies using Bayesian network classifiers

   Magister-uppsats, Umeå universitet/Statistik

   Författare :Oscar Öhman; [2019]
   Nyckelord :;

   Sammanfattning : Bayesian Network (BN) classifiers are a type of probabilistic models. The learning process consists of two steps, structure learning and parameter learning. Four BN classifiers will be learned. These are two different Naive Bayes classifiers (NB), one Tree Augmented Naive Bayes classifier (TAN) and one Forest Naive Bayes classifier (FAN). LÄS MER