- Location
-
Deutsche Nationalbibliothek Frankfurt am Main
- Dimensions
-
21 cm
- Extent
-
146 Seiten
- Language
-
Englisch
- Notes
-
Illustrationen
Universität zu Köln, Dissertation, 2016
- Keyword
-
Räumliche Statistik
Autokorrelation
Autoregressiver Prozess
Normalverteilung
Probabilistische Testtheorie
Ökonometrisches Modell
Lineares Regressionsmodell
Kriminalität
Stadt
Prognoseverfahren
Schätztheorie
USA
- Table of contents
- Rights
-
Bei diesem Objekt liegt nur das Inhaltsverzeichnis digital vor. Der Zugriff darauf ist unbeschränkt möglich.
- Last update
-
11.06.2025, 1:45 PM CEST
Data provider
This object is provided by:
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Object type
- Hochschulschrift
Associated
Time of origin
- [2016]
Other Objects (12)
Scalable Inference in Latent Gaussian Process Models
The node-wise Pseudo-marginal method: model selection with spatial dependence on latent graphs
Panel VAR models with spatial dependence
Calibrating cardiac electrophysiology models using latent Gaussian processes on atrial manifolds
Non-Linear Latent Variable Models for Inference and Learning from Non-Gaussian Data
A Structural Equation Approach to Spatial Dependence Models
Vector-autoregressive time series models with spatial dependence
Vector-autoregressive time series models with spatial dependence
Spatial location priors for Gaussian model based reverberant audio source separation
Estimation and visualization of process states using latent variable models based on Gaussian process
Disease mapping models for data with weak spatial dependence or spatial discontinuities
Incident Duration Prediction Based on Latent Gaussian Naive Bayesian classifier
Scalable Inference in Latent Gaussian Process Models
The node-wise Pseudo-marginal method: model selection with spatial dependence on latent graphs
Panel VAR models with spatial dependence
Calibrating cardiac electrophysiology models using latent Gaussian processes on atrial manifolds
Non-Linear Latent Variable Models for Inference and Learning from Non-Gaussian Data
A Structural Equation Approach to Spatial Dependence Models
Vector-autoregressive time series models with spatial dependence
Vector-autoregressive time series models with spatial dependence
Spatial location priors for Gaussian model based reverberant audio source separation
Estimation and visualization of process states using latent variable models based on Gaussian process
Disease mapping models for data with weak spatial dependence or spatial discontinuities
Incident Duration Prediction Based on Latent Gaussian Naive Bayesian classifier
Scalable Inference in Latent Gaussian Process Models
The node-wise Pseudo-marginal method: model selection with spatial dependence on latent graphs
Panel VAR models with spatial dependence
Calibrating cardiac electrophysiology models using latent Gaussian processes on atrial manifolds
Non-Linear Latent Variable Models for Inference and Learning from Non-Gaussian Data
A Structural Equation Approach to Spatial Dependence Models
Vector-autoregressive time series models with spatial dependence
Vector-autoregressive time series models with spatial dependence
Spatial location priors for Gaussian model based reverberant audio source separation
Estimation and visualization of process states using latent variable models based on Gaussian process
Disease mapping models for data with weak spatial dependence or spatial discontinuities