The expectation maximization algorithm enables parameter estimation in probabilistic models with incomplete data. In summary, the expectation maximization algorithm alternates between the steps of ...

Maximization and satisficing are two distinct decision-making styles that influence how individuals approach choices in various aspects of life. Maximizers strive to make the best possible ...

Abstract: This chapter covers the expectation maximization algorithm and its variants, which are used for joint state and parameter estimation. The presented algorithms include expectation ...

1 School of Computer and Software Engineering, Xihua University, Chengdu, Sichuan, China 2 School of Statistics and Computer Science, McGill University, Montreal, QC, Canada Influence maximization (IM ...

Expectation Maximization is used to find the maximum likelihood of the model parameter when model depends on unobserved or latent variables. Expectation Maximization was proposed 1977 by a paper ...

Abstract: The weighted sum-rate maximization (WSRMax) problem plays a central role in many network control and optimization methods, such as power control, link scheduling, cross-layer control, ...

In this paper we explore the issue of wealth maximization and the implied behavior of the firm, paying particular attention to the results discussed above and how they are affected by the existence of ...

If you use any of this code, please cite: T. Stoffregen and L. Kleeman, Event Cameras, Contrast Maximization and Reward Functions: An Analysis, The IEEE Conference on Computer Vision and Pattern ...

Rahmattalabi, Aida, Shahin Jabbari, Himabindu Lakkaraju, Phebe Vayanos, Max Izenberg, Ryan Brown, Eric Rice, and Milind Tambe. "Fair Influence Maximization: A Welfare Optimization Approach." ...

The simplicity and transparency of the business (e.g. marginal cost is easily observed) allow for direct tests of profit maximization in the quantities delivered each day and the prices that are ...