Harmonic Syntax of the Twelve-Bar Blues Form

2017 ◽  
Vol 35 (2) ◽  
pp. 165-192 ◽  
Author(s):  
Jonah Katz
Keyword(s):  
Bayesian Information Criterion ◽  
Regression Models ◽  
Information Criterion ◽  
Mixed Effects ◽  
Context Free Grammar ◽  
Harmonic Syntax ◽  
Novel Method ◽  
The Difference ◽  
Transitional Probabilities ◽  
Context Free

This paper describes the construction and analysis of a corpus of harmonic progressions from 12-bar blues forms included in the jazz repertoire collection The Real Book. A novel method of coding and analyzing such corpus data is developed, with a notion of “possible harmonic change” derived from the corpus and logit mixed-effects regression models that describe the difference between actually occurring harmonic events and possible but non-occurring ones in terms of various sets of theoretical constructs. Models using different sets of constructs are compared using the Bayesian Information Criterion, which assesses the accuracy and efficiency of each model. The principal results are that: (1) transitional probabilities are better modeled using root-motion and chord-frequency information than they are using pairs of individual chords; (2) transitional probabilities are better described using a mixture model intermediate in complexity between a bigram and full trigram model; and (3) the difference between occurring and non-occurring chords is more efficiently modeled with a hierarchical, recursive context-free grammar than it is as a Markov chain. The results have implications for theories of harmony, composition, and cognition more generally.

scholarly journals A Study of Count Regression Models for Mortality Rate

CAUCHY ◽  
2021 ◽  
Vol 7 (1) ◽  
pp. 142-151
Author(s):  
Anwar Fitrianto
Keyword(s):  
Mortality Rate ◽  
Regression Model ◽  
Count Data ◽  
Bayesian Information Criterion ◽  
Regression Models ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Information Criterion ◽  
Poisson Regression Model ◽  
Binomial Regression

This paper discusses how overdispersed count data to be fit. Poisson regression model, Negative Binomial 1 regression model (NEGBIN 1) and Negative Binomial regression 2 (NEGBIN 2) model were proposed to fit mortality rate data. The method used is comparing the values of Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) to find out which method suits the data the most. The results show that the data indeed display higher variability. Among the three models, the model preferred is NEGBIN 1 model.

scholarly journals Modelling Claim Frequency in Insurance Using Count Models

2021 ◽  
pp. 14-20
Author(s):  
A. Adetunji Ademola ◽  
Shamsul Rijal Muhammad Sabri
Keyword(s):  
Bayesian Information Criterion ◽  
Regression Models ◽  
Negative Binomial ◽  
Predictive Ability ◽  
Information Criterion ◽  
Information Criteria ◽  
Binomial Model ◽  
Count Models ◽  
Claim Frequency ◽  
Insurance Data

Background: In modelling claim frequency in actuary science, a major challenge is the number of zero claims associated with datasets. Aim: This study compares six count regression models on motorcycle insurance data. Methodology: The Akaike Information Criteria (AIC) and the Bayesian Information Criterion (BIC) were used for selecting best models. Results: Result of analysis showed that the Zero-Inflated Poisson (ZIP) with no regressors for the zero component gives the best predictive ability for the data with the least BIC while the classical Negative Binomial model gives the best result for explanatory purpose with the least AIC.

scholarly journals Estimates of covariance functions for growth of Anglo-Nubian goats

2011 ◽  
Vol 40 (1) ◽  
pp. 106-114 ◽  
Author(s):  
José Ernandes Rufino de Sousa ◽  
Martinho de Almeida e Silva ◽  
José Lindenberg Rocha Sarmento ◽  
Wandrick Hauss de Sousa ◽  
Maria do Socorro Medeiros de Souza ◽  
...  
Keyword(s):  
Bayesian Information Criterion ◽  
Regression Models ◽  
Genetic Effect ◽  
Information Criterion ◽  
Random Regression ◽  
Additive Genetic Effect ◽  
Ratio Test ◽  
Residual Variance ◽  
Covariance Functions ◽  
Heritability Estimates

It was used 4,313 weight records from birth to 196 days of age from 946 Anglo-nubiana breed goats, progenies from 43 sires and 279 dams, controlled in the period from 1980 to 2005, with the objective of estimating covariance functions and genetic parameters of animals by using random regression models. It was evaluated 12 random regression models, with degrees ranging from 1 to 7 for direct additive genetic and maternal and animal permanent environment effect and residual variance adjusted by using animal age ordinary polynomial of third order. Models were compared by using likelihood ratio test and by Bayesian information criterion of Schwarz and Akaike information criterion. The model selected based on Bayesian information criterion was the one that considered the maternal and direct additive genetic effect adjusted by a quadratic polynomial and the animal permanent environmental effect adjusted by a cubic polynomial (M334). Heritability estimates for direct effect were lower in the beginning and at the end of the studied period and maternal heritability estimates were higher at 196 days of age in comparison to the other growth phases. Genetic correlation ranged from moderate to high and they decreased as the distance between ages increased. Higher efficiency in selection for weight can be obtained by considering weights close to weaning, which is a period when the highest estimates of genetic variance and heritability are obtained.

Context Free Grammar Identification from Positive Samples

2018 ◽  
Vol 7 (3.12) ◽  
pp. 1096
Author(s):  
K Senthil Kumar ◽  
D Malathi
Keyword(s):  
Formal Language ◽  
Target Language ◽  
Language Theory ◽  
Grammatical Inference ◽  
Context Free Grammar ◽  
Chomsky Hierarchy ◽  
Compiler Design ◽  
Novel Method ◽  
Context Free

In grammatical inference one aims to find underlying grammar or automaton which explains the target language in some way. Context free grammar which represents type 2 grammar in Chomsky hierarchy has many applications in Formal Language Theory, pattern recognition, Speech recognition, Machine learning , Compiler design and Genetic engineering etc. Identification of unknown Context Free grammar of the target language from positive examples is an extensive area in Grammatical Inference/ Grammar induction. In this paper we propose a novel method which finds the equivalent Chomsky Normal form.  

Framework for rare event detection using Artificial Neural Network based context free grammar

2020 ◽  
Vol 39 (6) ◽  
pp. 8463-8475
Author(s):  
Palanivel Srinivasan ◽  
Manivannan Doraipandian
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Event Detection ◽  
Performance Metrics ◽  
Rare Events ◽  
Rare Event ◽  
Video Stream ◽  
Context Free Grammar ◽  
Artificial Neural ◽  
Context Free

Rare event detections are performed using spatial domain and frequency domain-based procedures. Omnipresent surveillance camera footages are increasing exponentially due course the time. Monitoring all the events manually is an insignificant and more time-consuming process. Therefore, an automated rare event detection contrivance is required to make this process manageable. In this work, a Context-Free Grammar (CFG) is developed for detecting rare events from a video stream and Artificial Neural Network (ANN) is used to train CFG. A set of dedicated algorithms are used to perform frame split process, edge detection, background subtraction and convert the processed data into CFG. The developed CFG is converted into nodes and edges to form a graph. The graph is given to the input layer of an ANN to classify normal and rare event classes. Graph derived from CFG using input video stream is used to train ANN Further the performance of developed Artificial Neural Network Based Context-Free Grammar – Rare Event Detection (ACFG-RED) is compared with other existing techniques and performance metrics such as accuracy, precision, sensitivity, recall, average processing time and average processing power are used for performance estimation and analyzed. Better performance metrics values have been observed for the ANN-CFG model compared with other techniques. The developed model will provide a better solution in detecting rare events using video streams.

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