Essays about: "Markov chain Monte Carlo MCMC"

Showing result 1 - 5 of 22 essays containing the words Markov chain Monte Carlo MCMC.

1. 1. Branching Out with Mixtures: Phylogenetic Inference That’s Not Afraid of a Little Uncertainty

   University essay from KTH/Matematisk statistik

   Author : Ricky Molén; [2023]
   Keywords : Phylogeny; Bayesian analysis; Markov chain Monte Carlo; Variational inference; Mixture of proposal distributions; Fylogeni; Bayesiansk analys; Markov Chain Monte Carlo; Variationsinferens; Mixturer av förslagsfördelningar;

   Abstract : Phylogeny, the study of evolutionary relationships among species and other taxa, plays a crucial role in understanding the history of life. Bayesian analysis using Markov chain Monte Carlo (MCMC) is a widely used approach for inferring phylogenetic trees, but it suffers from slow convergence in higher dimensions and is slow to converge. READ MORE

3. 2. Optimization and Bayesian Modeling of Road Distance for Inventory of Potholes in Gävle Municipality

   University essay from Stockholms universitet/Statistiska institutionen

   Author : Timothy Rafael Lindblom; Oskar Tollin; [2022]
   Keywords : Traveling salesman problem; Bayesian inference; Simulated annealing; Nearest neighbour algorithm; Markov Chain Monte Carlo; MCMC; Potholes; Haversine formula; Metropolis-Hastings; Posterior predictive distribution; Handelsresande problemet; bayesiansk inferens; simulerad anlöpning; nearest neighbour algorithm; Markov Chain Monte Carlo; MCMC; potthål; storcirkelavstånd; Metropolis-Hastings; posterior predictive distribution;

   Abstract : Time management and distance evaluation have long been a difficult task for workers and companies. This thesis studies 6712 pothole coordinates in Gävle municipality, and evaluates the minimal total road distance needed to visit each pothole once, and return to an initial pothole. READ MORE

4. 3. Spatial Statistical Modelling of Insurance Claim Frequency

   University essay from Lunds universitet/Matematisk statistik

   Author : Daniel Faller; [2022]
   Keywords : Insurance risk; claim frequency; Markov chain Monte Carlo MCMC ; Riemann manifold Metropolis adjusted Langevin algorithm MMALA ; spatial statistics; Gaussian Markov random field GMRF ; preconditioned Crank Nicolson Langevin algorithm pCNL ; Gibbs sampling; Bayesian hierarchical modelling; high dimensional; shrinkage prior; horseshoe prior; regularisation.; Mathematics and Statistics;

   Abstract : In this thesis a fully Bayesian hierarchical model that estimates the number of aggregated insurance claims per year for non-life insurances is constructed using Markov chain Monte Carlo based inference with Riemannian Langevin diffusion. Some versions of the model incorporate a spatial effect, viewed as the relative spatial insurance risk that originates from a policyholder's geographical location and where the relative spatial insurance risk is modelled as a continuous spatial field. READ MORE

5. 4. Markov Chain Monte Carlo (MCMC) and Bayesian Inference for Gravitational Waves

   University essay from Lunds universitet/Astronomi - Genomgår omorganisation

   Author : Christine Andersson; [2021]
   Keywords : Gravitational Waves; LISA; LISA mission; Bayesian Inference; Markov Chain Monte Carlo; MCMC; Bayes’ Theorem; stochastic sampling; Metropolis Hastings; histograms; Physics and Astronomy;

   Abstract : The Laser Interferometer Space Antenna (LISA) is a space borne gravitational wave detec- tor set to launch in 2034, with the objective of detecting and studying the Gravitational Waves (GWs) of our universe. So far, ground-based detectors such as the Laser Interferometer Gravitational-Wave Observatory (LIGO) have been successful in detecting GWs, but the limitations of ground based detectors is what makes LISA so special. READ MORE

6. 5. Efficient Sampling of Gaussian Processes under Linear Inequality Constraints

   University essay from Linköpings universitet/Statistik och maskininlärning

   Author : Bayu Beta Brahmantio; [2021]
   Keywords : Gaussian process; truncated multivariate Gaussian; Hamiltonian Monte Carlo; Elliptical Slice Sampling;

   Abstract : In this thesis, newer Markov Chain Monte Carlo (MCMC) algorithms are implemented and compared in terms of their efficiency in the context of sampling from Gaussian processes under linear inequality constraints. Extending the framework of Gaussian process that uses Gibbs sampler, two MCMC algorithms, Exact Hamiltonian Monte Carlo (HMC) and Analytic Elliptical Slice Sampling (ESS), are used to sample values of truncated multivariate Gaussian distributions that are used for Gaussian process regression models with linear inequality constraints. READ MORE