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Showing result 1 - 5 of 33 essays matching the above criteria.

  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. Economic Capital Models : Methods for fitting loss distributions

    University essay from Umeå universitet/Institutionen för matematik och matematisk statistik

    Author : William Fritzell; [2023]
    Keywords : Economic Capital; Distribution fitting; MCMC;

    Abstract : The thesis provides a well-researched classical approach to fit and predict the losses (extreme) for Lloyds Bank’s Dutch mortgage portfolio, their defaulted Dutch mortgage portfolio, and their German personal and car loan portfolio. This is a crucial piece for quantification of the economic loss, required for effective credit risk management by the Bank. READ MORE

  4. 3. Investigating a possible dearth of stars with zero angular momentum in the solar neighbourhood.

    University essay from Lunds universitet/Astronomi - Genomgår omorganisation; Lunds universitet/Fysiska institutionen

    Author : Aayush Desai; [2023]
    Keywords : Galaxy: disk; Galaxy: fundamental parameters; Galaxy: kinematics and dynamics; Galaxy: nucleus; solar neighborhood; stars: kinematics and dynamics; Physics and Astronomy;

    Abstract : Background. Carlberg & Innanen (1987) first proposed the idea of measuring the solar re- flex velocity (the velocity of the Sun around the galactic centre), Vg,⊙, using the momenta of the stars in the solar neighbourhood. The proposal was a consequence of the work of L. READ MORE

  5. 4. 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

  6. 5. 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