Sökning: "Kullback divergence"
Visar resultat 1 - 5 av 12 avhandlingar innehållade orden Kullback divergence.
1. Performance and Implementation Aspects of Nonlinear Filtering
Sammanfattning : I många fall är det viktigt att kunna få ut så mycket och så bra information som möjligt ur tillgängliga mätningar. Att utvinna information om till exempel position och hastighet hos ett flygplan kallas för filtrering. I det här fallet är positionen och hastigheten exempel på tillstånd hos flygplanet, som i sin tur är ett system. LÄS MER
2. Maximum spacing methods and limit theorems for statistics based on spacings
Sammanfattning : The maximum spacing (MSP) method, introduced by Cheng and Amin (1983) and independently by Ranneby (1984), is a general estimation method for continuous univariate distributions. The MSP method, which is closely related to the maximum likelihood (ML) method, can be derived from an approximation based on simple spacings of the Kullback-Leibler information. LÄS MER
3. Semi Markov chain Monte Carlo
Sammanfattning : The first paper introduces a new simulation technique, called semi Markov chain Monte Carlo, suitable for estimating the expectation of a fixed function over a distribution π, Eπf(χ). Given a Markov chain with stationary distribution p, for example a Markov chain corresponding to a Markov chain Monte Carlo algorithm, an embedded Markov renewal process is used to divide the trajectory into different parts. LÄS MER
4. Diagnosability performance analysis of models and fault detectors
Sammanfattning : Model-based diagnosis compares observations from a system with predictions using a mathematical model to detect and isolate faulty components. Analyzing which faults that can be detected and isolated given the model gives useful information when designing a diagnosis system. LÄS MER
5. Change point detection with respect to variance
Sammanfattning : This thesis examines a simple method for detecting a change with respect to the variance in a sequence of independent normally distributed observations with a constant mean. The method filters out observations with extreme values and divides the sequence into equally large subsequences. LÄS MER