Global Data Flow Analysis and Iterative Algorithms

Iterative Data-flow Analysis, Revisited

The iterative algorithm is widely used to solve instances of data-flow analysis problems. The algorithm is attractive because it is easy to implement and robust in its behavior. The theory behind the algorithm shows that, for a broad class of problems, it terminates and produces correct results. The theory also establishes a set of conditions where the algorithm runs in at most d(G) + 3 passes ...

Data Flow Representation of Iterative Algorithms for Systolic Arrays

Iterative Flow Analysis

Control and data ow information is vital for the optimization of high level programming languages. Language features such as object-orientation and rst class functions and selectors link data ow and control ow. For example, in an object-oriented program an object's run time type is used to determine the function (method) executed at an invocation point via dynamic dispatch. We present an iterat...

Bayesian and Iterative Maximum Likelihood Estimation of the Coefficients in Logistic Regression Analysis with Linked Data

This paper considers logistic regression analysis with linked data. It is shown that, in logistic regression analysis with linked data, a finite mixture of Bernoulli distributions can be used for modeling the response variables. We proposed an iterative maximum likelihood estimator for the regression coefficients that takes the matching probabilities into account. Next, the Bayesian counterpart...

Financial Reporting Fraud Detection: An Analysis of Data Mining Algorithms

In the last decade, high profile financial frauds committed by large companies in both developed and developing countries were discovered and reported. This study compares the performance of five popular statistical and machine learning models in detecting financial statement fraud. The research objects are companies which experienced both fraudulent and non-fraudulent financial statements betw...