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Visar resultat 1 - 5 av 391 avhandlingar som matchar ovanstående sökkriterier.

1. 1. Sequential Monte Carlo methods for conjugate state-space models

   Författare :Anna Wigren; Fredrik Lindsten; Lawrence Murray; Riccardo Sven Risuleo; Simon Maskell; Uppsala universitet; []
   Nyckelord :TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; Sequential Monte Carlo; Particle filter; Markov chain Monte Carlo; Conjugacy; State-space model; Probabilistic programming; Electrical Engineering with specialization in Signal Processing; Elektroteknik med inriktning mot signalbehandling;

   Sammanfattning : Bayesian inference in state-space models requires the solution of high-dimensional integrals, which is intractable in general. A viable alternative is to use sample-based methods, like sequential Monte Carlo, but this introduces variance into the inferred quantities that can sometimes render the estimates useless. LÄS MER

3. 2. Development of New Monte Carlo Methods in Reactor Physics : Criticality, Non-Linear Steady-State and Burnup Problems

   Författare :Jan Dufek; Jan Wallenius; Waclaw Gudowski; Cheikh Diop; KTH; []
   Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; Monte Carlo; reactor physics; fission source; inactive cycles; convergence; burnup; steady-state; criticality; eigenvalue; Nuclear physics; Kärnfysik;

   Sammanfattning : The Monte Carlo method is, practically, the only approach capable of giving detail insight into complex neutron transport problems. In reactor physics, the method has been used mainly for determining the keff in criticality calculations. LÄS MER

4. 3. Accelerating Monte Carlo methods for Bayesian inference in dynamical models

   Författare :Johan Dahlin; Thomas B. Schön; Fredrik Lindsten; Richard Everitt; Linköpings universitet; []
   Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; NATURVETENSKAP; NATURAL SCIENCES; Computational statistics; Monte Carlo; Markov chains; Particle filters; Machine learning; Bayesian optimisation; Approximate Bayesian Computations; Gaussian processes; Particle Metropolis-Hastings; Approximate inference; Pseudo-marginal methods;

   Sammanfattning : Making decisions and predictions from noisy observations are two important and challenging problems in many areas of society. Some examples of applications are recommendation systems for online shopping and streaming services, connecting genes with certain diseases and modelling climate change. LÄS MER

5. 4. Machine learning using approximate inference : Variational and sequential Monte Carlo methods

   Författare :Christian Andersson Naesseth; Thomas Schön; Fredrik Lindsten; Iain Murray; Linköpings universitet; []
   Nyckelord :TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; NATURVETENSKAP; NATURAL SCIENCES; TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY;

   Sammanfattning : Automatic decision making and pattern recognition under uncertainty are difficult tasks that are ubiquitous in our everyday life. The systems we design, and technology we develop, requires us to coherently represent and work with uncertainty in data. LÄS MER

7. 5. Hierarchical Variance Reduction Techniques for Monte Carlo Rendering

   Författare :Petrik Clarberg; Institutionen för datavetenskap; []
   Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; computer graphics; Monte Carlo methods; importance sampling; hierarchical techniques; rendering;

   Sammanfattning : Ever since the first three-dimensional computer graphics appeared half a century ago, the goal has been to model and simulate how light interacts with materials and objects to form an image. The ultimate goal is photorealistic rendering, where the created images reach a level of accuracy that makes them indistinguishable from photographs of the real world. LÄS MER