In this paper, we study optimal pricing for revenue maximization in the presence of positive network externalities. In our model, the value of a digital good for a buyer is a function of the set of buyers who have already bought the item. In this setting, a buyer’s decision to buy an item depends on the price of the item as well as the set of other buyers that own the item. The revenue maximiza...

Today's competitive conditions have caused the projects to be carried out in the least possible time with limited resources. Therefore, managing and scheduling a project is a necessity for the project. The timing of a project is to specify a sequence of times for a series of related activities. According to their priority and their latency, so that between the time the project is completed and ...

We present the regression-based Monte Carlo simulation algorithms for solving the stochastic control models associated with pricing and hedging of the Guaranteed Lifelong Withdrawal Benefit (GLWB) in variable annuities, where the dynamics of the underlying fund value is assumed to evolve according to the stochastic volatility model. The GLWB offers a lifelong withdrawal benefit even when the po...

Software-as-a-Service (SaaS) models are gaining acceptance but their business value has largely been conceptual or anecdotal. In this paper, we empirically examine the role of SaaS in IT-enabled innovation of the firms and the role of organizational complementarities in augmenting the impact. We argue that SaaS can support IT-enabled innovation and that complementarities like learning from past...

The base line model in scholarly research on decision-making has usually been a rational model where the inherent ideology is value maximization through efficiency and effectiveness. However, other decision models have been needed to explain those organizational choices that do not satisfy the conditions of rationality. In this paper automatic decision making, or automaticity, is argued to be o...

In this paper, we firstly give a brief introduction of expectation maximization (EM) algorithm, and then discuss the initial value sensitivity of expectation maximization algorithm. Subsequently, we give a short proof of EM's convergence. Then, we implement experiments with the expectation maximization algorithm (We implement all the experiments on Gaussion mixture model (GMM) ). Our experiment...

The abundance of models of complex networks and the current insufficient validation standards make it difficult to judge which models are strongly supported by data and which are not. We focus here on likelihood maximization methods for models of growing networks with many parameters and compare their performance on artificial and real datasets. While high dimensionality of the parameter space ...

abstract- this study aims to model optimal cultivation pattern based on the maximization of importing virtual water and social net income of major crops in marvdasht, iran. for this purpose, a linear programming model was developed considering constraints of virtual water and land limitations followed by other models to include employment restrictions. based on the findings, wheat and tomato we...

Iterative Proportional Fitting (IPF), com­ bined with EM, is commonly used as an al­ gorithm for likelihood maximization in undi­ rected graphical models. In this paper, we present two iterative algorithms that gener­ alize upon IPF. The first one is for likelihood maximization in discrete chain factor graphs, which we define as a wide class of discrete variable models including undirected grap...

Recently, analysis of structural equation models with polytomous and continuous variables has received a lot of attention. However, contributions to the selection of good models are limited. The main objective of this article is to investigate the maximum likelihood estimation of unknown parameters in a general LISREL-type model with mixed polytomous and continuous data and propose a model sele...