- GRStats2
- Posts : 74
Join date : 2022-11-19
AUEB Stats Seminars 3/11/2023: Bayesian nonparametrics & Random dynamical systems by Konstantinos Kaloudis (Department of Statistics and Actuarial-Financial Mathematics, University of the Aegean)
Tue 24 Oct 2023 - 17:27
AUEB STATS SEMINARS 2023
Konstantinos Kaloudis
Department of Statistics and Actuarial-Financial Mathematics, University of the Aegean
Title: Bayesian nonparametrics & Random dynamical systems
FRIDAY, 3/11/2023
13:15
Room T105, Troias Building
ABSTRACT
The normality of the (dynamical) noise process is one of the most common assumptions in the Random dynamical systems literature. In this talk, we will present a unified methodological framework, under the Bayesian nonparametric modeling approach, useful for the approximation of dynamical invariants based on observed time-series data. Specifically, we will use the Dirichlet Process and Geometric Stick-Breaking Process random measures as priors over the noise density. The presented methods can also be used for reconstruction, prediction and noise reduction purposes, under the assumption of a known functional form of the data-generating process and a symmetric (non-gaussian) noise density. Finally, we will discuss a recent extension of our framework, based on the utilization of Bayesian Neural Networks.
Konstantinos Kaloudis
Department of Statistics and Actuarial-Financial Mathematics, University of the Aegean
Title: Bayesian nonparametrics & Random dynamical systems
FRIDAY, 3/11/2023
13:15
Room T105, Troias Building
ABSTRACT
The normality of the (dynamical) noise process is one of the most common assumptions in the Random dynamical systems literature. In this talk, we will present a unified methodological framework, under the Bayesian nonparametric modeling approach, useful for the approximation of dynamical invariants based on observed time-series data. Specifically, we will use the Dirichlet Process and Geometric Stick-Breaking Process random measures as priors over the noise density. The presented methods can also be used for reconstruction, prediction and noise reduction purposes, under the assumption of a known functional form of the data-generating process and a symmetric (non-gaussian) noise density. Finally, we will discuss a recent extension of our framework, based on the utilization of Bayesian Neural Networks.
- AUEB-Stats Seminars 12/4/2024: Bayesian inference with variable-memory models for times series by Ioannis Kontoyiannis (Professor | Department of Pure Mathematics and Mathematical Statistics, University of Cambridge, UK)
- AUEB Stats Seminars 28/4/2023: The dynamics of AI by Panayotis Mertikopoulos (Department of Mathematics, National and Kapodistrian University of Athens)
- AUEB-Stats Seminars 10/11/2023: Visualizing stability in studies: the moving average meta-analysis by Konstantinos Pateras (Department of Public and One Health, University of Thessaly)
- AUEB-Stats Seminars 14/12/2023: Bayesian Methods for the Integration of Historical Data by Roberto Macrì Demartino (Department of Statistical Sciences, University of Padua (IT))
- AUEB-Stats Seminars 16/10/2024: "Bayesian Signature Authenticity Validation", by Ioannis Ntzoufras (Department of Statistics, Athens University of Economics and Business)
Permissions in this forum:
You cannot reply to topics in this forum