Sökning: "Missing not at Random"
Visar resultat 1 - 5 av 13 uppsatser innehållade orden Missing not at Random.
1. Detecting Metro Service Disruptions and Predicting their Spillover Effects throughout the Network using GTFS and Large-Scale Vehicle Location Data
Master-uppsats, KTH/TransportplaneringSammanfattning : One of the top factors that influence commuters’ satisfaction level with public transport is the punctuality of the service. Commuters rely on public transport to get them from their origin to destination on time and any form of delay will incur additional cost to both the commuters as well as the public transport operators. LÄS MER
2. Diffusion Models for Video Prediction and Infilling : Training a conditional video diffusion model for arbitrary video completion tasks
Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)Sammanfattning : To predict and anticipate future outcomes or reason about missing information in a sequence is a key ability for agents to be able to make intelligent decisions. This requires strong temporally coherent generative capabilities. LÄS MER
3. Missing Data - A Gentle Introduction
Master-uppsats, Uppsala universitet/Statistiska institutionenSammanfattning : This thesis provides an introduction to methods for handling missing data. A thorough review of earlier methods and the development of the field of missing data is provided. The thesis present the methods suggested in today’s literature, multiple imputation and maximum likelihood estimation. LÄS MER
4. Object Tracking Achieved by Implementing Predictive Methods with Static Object Detectors Trained on the Single Shot Detector Inception V2 Network
Master-uppsats, Karlstads universitet/Fakulteten för hälsa, natur- och teknikvetenskap (from 2013)Sammanfattning : In this work, the possibility of realising object tracking by implementing predictive methods with static object detectors is explored. The static object detectors are obtained as models trained on a machine learning algorithm, or in other words, a deep neural network. LÄS MER
5. Deep Learning techniques for classification of data with missing values
Magister-uppsats, Lunds universitet/Beräkningsbiologi och biologisk fysik - Genomgår omorganisationSammanfattning : Two deep learning techniques for classification on corrupt data are investigated and compared by performance. A simple imputation before classification is compared to imputation using a Variational Autoencoder (VAE). LÄS MER