Bayesian learning models with revision of evidence

Philosophia ◽  
1978 ◽  
Vol 7 (2) ◽  
pp. 357-367 ◽  
Author(s):  
William Harper
Keyword(s):  
Bayesian Learning ◽  
Learning Models

scholarly journals Performance-Induced CEO Turnover

2020 ◽  
Author(s):  
Dirk Jenter ◽  
Katharina Lewellen
Keyword(s):  
Firm Performance ◽  
Bayesian Learning ◽  
Ceo Turnover ◽  
Learning Models ◽  
Match Quality ◽  
Highly Correlated ◽  
The Relationship ◽  
As If

Abstract This paper revisits the relationship between firm performance and CEO turnover. Instead of classifying turnovers into forced and voluntary, we introduce performance-induced turnover, defined as turnover that would not have occurred had performance been “good.” We document a close turnover-performance link and estimate that 38%–55% of turnovers are performance induced. This is significantly more than the number of forced turnovers, though the two types of turnovers are highly correlated. Compared to the predictions of Bayesian learning models, learning about CEO ability appears to be slow, and boards act as if CEO ability (or match quality) was subject to frequent shocks.

EVALUATING INTONATIONAL FEATURES FOR EMOTION RECOGNITION FROM SPEECH

2007 ◽  
Vol 16 (06) ◽  
pp. 1001-1014 ◽  
Author(s):  
PANAGIOTIS ZERVAS ◽  
IOSIF MPORAS ◽  
NIKOS FAKOTAKIS ◽  
GEORGE KOKKINAKIS
Keyword(s):  
Machine Learning ◽  
Decision Tree ◽  
Emotion Recognition ◽  
Bayesian Learning ◽  
Experimental Results ◽  
Speech Signals ◽  
Learning Approaches ◽  
Learning Models ◽  
C4.5 Decision Tree ◽  
Machine Learning Models

This paper presents and discusses the problem of emotion recognition from speech signals with the utilization of features bearing intonational information. In particular parameters extracted from Fujisaki's model of intonation are presented and evaluated. Machine learning models were build with the utilization of C4.5 decision tree inducer, instance based learner and Bayesian learning. The datasets utilized for the purpose of training machine learning models were extracted from two emotional databases of acted speech. Experimental results showed the effectiveness of Fujisaki's model attributes since they enhanced the recognition process for most of the emotion categories and learning approaches helping to the segregation of emotion categories.

Variational Bayesian Learning Theory

2019 ◽  
Author(s):  
Shinichi Nakajima ◽  
Kazuho Watanabe ◽  
Masashi Sugiyama
Keyword(s):  
Learning Theory ◽  
Bayesian Learning ◽  
Variational Bayesian
Sign in / Sign up

Export Citation Format

Share Document