Sökning: "stochastic simulation"
Visar resultat 1 - 5 av 186 uppsatser innehållade orden stochastic simulation.
1. Geometry of high dimensional Gaussian data
Kandidat-uppsats, Linköpings universitet/Tillämpad matematik; Linköpings universitet/Tekniska fakultetenSammanfattning : Collected data may simultaneously be of low sample size and high dimension. Such data exhibit some geometric regularities consisting of a single observation being a rotation on a sphere, and a pair of observations being orthogonal. This thesis investigates these geometric properties in some detail. LÄS MER
2. Simuleringsdriven inferens av stokastiska dynamiska system
Kandidat-uppsats, Göteborgs universitet/Institutionen för matematiska vetenskaperSammanfattning : Stokastiska modeller, som ger tillförlitlig och användbar information om ett systems beteende, består ofta av stokastiska differentialekvationer (SDE) vars likelihoodfunktion inte är analytiskt tillgänglig. Mer traditionella Markov Chain Monte Carlo-metoder (MCMC) samt relativt nyligen utvecklade likelihood-fria Approximate Bayesian Computation-metoder (ABC) utgör populära angrepssätt för att utföra inferens på dessa typer av problem. LÄS MER
3. Simulated molecular adder circuits on a surface of DNA : Studying the scalability of surface chemical reaction network digital logic circuits
Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)Sammanfattning : The behavior of the Deoxyribonucleic Acid (DNA) molecule can be exploited to perform useful computation. It can also be ”programmed” using the language of Chemical Reaction Networks (CRNs). One specialized CRN construct is the Surface Chemical Reaction Network (SCRN). LÄS MER
4. Parameter Inference for Stochastic Models of Gene Expression in Eukaryotic Cells
Kandidat-uppsats, Uppsala universitet/Institutionen för informationsteknologiSammanfattning : Simulation models are often used to study a system or phenomenon. However, before a simulation model can be used, its parameter needs to be fit to mimic observed data. This is called the parameter inference problem. LÄS MER
5. Determining Protein Conformational Ensembles by Combining Machine Learning and SAXS
Master-uppsats, KTH/Tillämpad fysikSammanfattning : In structural biology, immense effort has been put into discovering functionally relevant atomic resolution protein structures. Still, most experimental, computational and machine learning-based methods alone struggle to capture all the functionally relevant states of many proteins without very involved and system-specific techniques. LÄS MER