Sökning: "stokastisk process"
Visar resultat 11 - 15 av 42 uppsatser innehållade orden stokastisk process.
11. Forward start options in Heston model
Master-uppsats, Lunds universitet/Matematisk statistikSammanfattning : En undersökning om stokastisk volatilitet för Forward start optioner, kan också användas för cliquet- optioner. Heston parameteriseringen användes. Det är i klassen AJD, av Duffie-Pan- Singleton.. LÄS MER
12. Particle Filter Bridge Interpolation in GANs
Master-uppsats, KTH/Matematisk statistikSammanfattning : Generative adversarial networks (GANs), a type of generative modeling framework, has received much attention in the past few years since they were discovered for their capacity to recover complex high-dimensional data distributions. These provide a compressed representation of the data where all but the essential features of a sample is extracted, subsequently inducing a similarity measure on the space of data. LÄS MER
13. Numerical Modelling of Gas Atomised Metal Droplets
Master-uppsats, KTH/MaterialvetenskapSammanfattning : The ability to predict nucleation and solidification behaviour is essential for producing high-quality metal powders. The droplet cooling of complex multi-component alloy systems has not been accurately studied by any models to date. LÄS MER
14. Model for risk evaluation for fragment debris after a grenade detonation
Uppsats för yrkesexamina på avancerad nivå, Karlstads universitet/Fakulteten för hälsa, natur- och teknikvetenskap (from 2013)Sammanfattning : Accidents when using or storing explosives can lead to a large number of casualties and injuries. Hence, it is of vital importance, in all countries, to know the risk and act responsibly when working with explosives. LÄS MER
15. A Study of the Loss Landscape and Metastability in Graph Convolutional Neural Networks
Master-uppsats, KTH/Matematisk statistikSammanfattning : Many novel graph neural network models have reported an impressive performance on benchmark dataset, but the theory behind these networks is still being developed. In this thesis, we study the trajectory of Gradient descent (GD) and Stochastic gradient descent (SGD) in the loss landscape of Graph neural networks by replicating Xing et al. LÄS MER