Time series-data accompanied with a sequential ordering-occur and evolve all around us. Analysing time series is the problem of trying to discern and describe a pattern in the sequential data that develops in a logical way as the series continues, and the study of sequential data has occurred for a long period across a vast array of fields, including signal processing, bioinformatics, and finance-to name but a few. Classical approaches are based on estimating the parameters of temporal evolution of the process according to an assumed model. In econometrics literature, the field is focussed on parameter estimation of linear (regression) models with a number of extensions. In this thesis, I take a Bayesian probabilistic modelling approach in ...
The present PhD dissertation consists of two independent job-market papers, therefore each chapter r...
We provide a flexible means of estimating time-varying parameter models in a Bayesian framework. By...
We discuss Bayesian model uncertainty analysis and forecasting in sequential dynamic modeling of mul...
Time series-data accompanied with a sequential ordering-occur and evolve all around us. Analysing ti...
"'What's going to happen next?' Time series data hold the answers, and Bayesian methods represent th...
In many applications data are collected sequentially in time with very short time intervals between ...
This thesis is a collection of three self-contained essays on using sequential Bayesian methods toge...
This thesis argues in favour of Bayesian techniques for the analysis of non- stationary linear time ...
This paper offers an approach to time series modeling that attempts to reconcile classical and Bayesi...
Many processes evolve over time and statistical models need to be adaptive to change. This thesis pr...
Sequential data (a.k.a. time-series) a rise in a multitude of different fields such as bi oinformati...
There is a one-to-one mapping between the conventional time series parameters of a third-order autor...
This paper offers a general approach to time series modeling that attempts to reconcile classical and...
An efficient method for Bayesian inference in stochastic volatility models uses a linear state space...
The analysis of time series data is important in fields as disparate as the social sciences, biology...
The present PhD dissertation consists of two independent job-market papers, therefore each chapter r...
We provide a flexible means of estimating time-varying parameter models in a Bayesian framework. By...
We discuss Bayesian model uncertainty analysis and forecasting in sequential dynamic modeling of mul...
Time series-data accompanied with a sequential ordering-occur and evolve all around us. Analysing ti...
"'What's going to happen next?' Time series data hold the answers, and Bayesian methods represent th...
In many applications data are collected sequentially in time with very short time intervals between ...
This thesis is a collection of three self-contained essays on using sequential Bayesian methods toge...
This thesis argues in favour of Bayesian techniques for the analysis of non- stationary linear time ...
This paper offers an approach to time series modeling that attempts to reconcile classical and Bayesi...
Many processes evolve over time and statistical models need to be adaptive to change. This thesis pr...
Sequential data (a.k.a. time-series) a rise in a multitude of different fields such as bi oinformati...
There is a one-to-one mapping between the conventional time series parameters of a third-order autor...
This paper offers a general approach to time series modeling that attempts to reconcile classical and...
An efficient method for Bayesian inference in stochastic volatility models uses a linear state space...
The analysis of time series data is important in fields as disparate as the social sciences, biology...
The present PhD dissertation consists of two independent job-market papers, therefore each chapter r...
We provide a flexible means of estimating time-varying parameter models in a Bayesian framework. By...
We discuss Bayesian model uncertainty analysis and forecasting in sequential dynamic modeling of mul...