Asim Roy argues that Islam in Bengal was not a corruption of the real"" Middle Eastern Islam, as nineteenth-century reformers claimed, but a valid historical religion developed in an area totally different from the Middle East. Originally published in 1984. The Princeton Legacy Library uses the latest print-on-demand technology to again make available previously out-of-print books from the distinguished backlist of Princeton University Press. These paperback editions preserve the original texts of these important books while presenting them in durable paperback editions
ABSTRACTThis paper models the corporate takeover process as a bargaining game under certainty. During the takeover process, an acquirer is generally uncertain about the minimum price the target shareholders will accept. Normally, a takeover is concluded after a sequence of offers have been made. This paper derives optimal offer strategies for the buyer at each stage of this bargaining game under uncertainty. Uncertainty about the target's minimum acceptable price is represented by a probability distribution. Optimal offer strategies depend on the probability distribution of the minimum acceptable price, which can change during the offer process.
ABSTRACTIn the last couple of decades, data analytics‐based pattern classification methods for disease detection have gained much traction in healthcare research and applications. The current study builds linear programming (LP) models for detecting disease incidence. We propose sequential steps of a convex programming algorithm to construct decision boundary functions to classify patterns in disease detection data. We compare the performance of our LP‐based classifier with others (neural network, decision tree, k‐nearest‐neighbor, logistic regression, naïve‐Bayes, and support‐vector‐machine) on four datasets: two different ones for breast cancer, and one each for diabetes and diabetic retinopathy. Statistical tests reveal that the LP classifier did significantly better than the other methods in five out of eight false‐positive and false‐negative test cases. There is not a statistically significant difference in performance in the remaining three tests between the LP classifier and the best alternative method. Most importantly, the LP classifier has significantly superior performance in both diabetes detection and diabetic retinopathy data. The success of the proposed LP classifier results from avoiding "modeling noise" and "memorization of training data." We recommend that our proposed LP classifier be among the set of classifiers for use in disease detection analytics.
This paper presents a general method for interactively searching for objects (alternatives) in a large collection the contents of which are unknown to the user and where the objects are defined by a large number of discrete-valued attributes. Briefly, the method presents an object and asks the user to indicate his or her preference for the object. The method allows preference indications in two basic modes: (1) by assignment of objects to predefined preference categories such as high, medium, and low preference or (2) by direct preference comparison of objects such as "object A preferred to object B." From these preference statements, the method learns about the user's preferences and constructs an approximation to a value or preference function of the user (additive or multiplicative) at each iteration. It then uses this approximate preference function to rerank the objects in the collection and retrieve the top-ranked ones to present to the user at the next iteration. The process terminates when the user is satisfied with the list of top-ranked objects. This method can also be used to solve general multiattribute discrete alternative problems, where the alternatives are known with certainty and described by a set of discrete-valued attributes. Test results are reported and application possibilities are discussed.
