The Choice of Sample Size for Mortality Forecasting: A Bayesian Learning Approach

2014 ◽  
Author(s):  
Hong Li ◽  
Anja De Waegenaere ◽  
Bertrand Melenberg
Keyword(s):  
Sample Size ◽  
Bayesian Learning ◽  
Learning Approach ◽  
Mortality Forecasting

MAS Learning Based on Bayesian Learning Method

2010 ◽  
Vol 20-23 ◽  
pp. 1292-1298
Author(s):  
De Jia Shi ◽  
Zhi Qiang Liu ◽  
Jing He
Keyword(s):  
Learning Strategies ◽  
Bayesian Learning ◽  
Free Text ◽  
Learning Approach ◽  
Learning Method ◽  
Multi Agent System ◽  
Agent System ◽  
Agent Learning ◽  
Multi Agent ◽  
Keyword Searches

Mulit-agent system[MAS] research on learning has been in the area of negotiation, and learning strategies of other agents.This paper presents an agent learning approach in multi-agent system based on Bayesian learning, it researches to develop agents that learn free-text queries and keyword searches in MAS. The MAS learns to identify an appropriate agent to answer free-text and natural language queries as well as keyword searches submitted by users. The paper describes how Bayesian learning is implemented in MAS, and analyzes the effectiveness of MAS learning based on the Bayesian learning approach by analyzing the accuracy and degree of learning.

scholarly journals Dealing with Drift Uncertainty: A Bayesian Learning Approach

2018 ◽  
Author(s):  
Carmine De Franco ◽  
Johann Nicolle ◽  
Huyen Pham
Keyword(s):  
Bayesian Learning ◽  
Learning Approach ◽  
Drift Uncertainty

scholarly journals Dealing with Drift Uncertainty: A Bayesian Learning Approach

Risks ◽  
2019 ◽  
Vol 7 (1) ◽  
pp. 5 ◽  
Author(s):  
Carmine De Franco ◽  
Johann Nicolle ◽  
Huyên Pham
Keyword(s):  
Bayesian Learning ◽  
Learning Strategy ◽  
Value Added ◽  
Learning Approach ◽  
Practical Procedure ◽  
Asset Classes ◽  
Two Parameters ◽  
Markowitz Problem ◽  
Drift Uncertainty ◽  
Smart Beta

One of the main challenges investors have to face is model uncertainty. Typically, the dynamic of the assets is modeled using two parameters: the drift vector and the covariance matrix, which are both uncertain. Since the variance/covariance parameter is assumed to be estimated with a certain level of confidence, we focus on drift uncertainty in this paper. Building on filtering techniques and learning methods, we use a Bayesian learning approach to solve the Markowitz problem and provide a simple and practical procedure to implement optimal strategy. To illustrate the value added of using the optimal Bayesian learning strategy, we compare it with an optimal nonlearning strategy that keeps the drift constant at all times. In order to emphasize the prevalence of the Bayesian learning strategy above the nonlearning one in different situations, we experiment three different investment universes: indices of various asset classes, currencies and smart beta strategies.

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