Sökning: "Uninformative Priors"

Hittade 3 avhandlingar innehållade orden Uninformative Priors.

  1. 1. DSGE Model Estimation and Labor Market Dynamics

    Författare :Glenn Mickelsson; Nils Gottfries; Karl Walentin; Martin M Andreasen; Uppsala universitet; []
    Nyckelord :SAMHÄLLSVETENSKAP; SOCIAL SCIENCES; DSGE Models; Macroeconomics; Estimation; Uninformative Priors; Maximum Likelihood; Labor Hoarding; US Labor Market; Swedish Micro Data; Economics; Nationalekonomi;

    Sammanfattning : Essay 1: Estimation of DSGE Models with Uninformative PriorsDSGE models are typically estimated using Bayesian methods, but because prior information may be lacking, a number of papers have developed methods for estimation with less informative priors (diffuse priors). This paper takes this development one step further and suggests a method that allows full information maximum likelihood (FIML) estimation of a medium-sized DSGE model. LÄS MER

  3. 2. Statistical inference with deep latent variable models

    Författare :Najmeh Abiri; Beräkningsbiologi och biologisk fysik - Genomgår omorganisation; []
    Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; Deep Learning; Generative Models; Variational Inference; Missing data; Imputation; Fysicumarkivet A:2019:Abiri;

    Sammanfattning : Finding a suitable way to represent information in a dataset is one of the fundamental problems in Artificial Intelligence. With limited labeled information, unsupervised learning algorithms help to discover useful representations. LÄS MER

  4. 3. Essays on forecasting and Bayesian model averaging

    Författare :Jana Eklund; Handelshögskolan i Stockholm; []
    Nyckelord :NATURVETENSKAP; NATURAL SCIENCES;

    Sammanfattning : This thesis, which consists of four chapters, focuses on forecasting in a data-rich environment and related computational issues. Chapter 1, “An embarrassment of riches: Forecasting using large panels” explores the idea of combining forecasts from various indicator models by using Bayesian model averaging (BMA) and compares the predictive performance of BMA with predictive performance of factor models. LÄS MER