Results 101 to 110 of about 557,766 (311)

Imputing unknown competitor marketing activity with a Hidden Markov Chain

, 2014
We demonstrate on a case study with two competing products at a bank how one can use a Hidden Markov Chain (HMC) to estimate missing information on a competitor's marketing activity.
Haughton, Dominique, Hua, Guangying, Jin, Danny, Lin, John, Wei, Qizhi, Zhang, Changan   +5 more
core   +2 more sources

Adaptive Autonomy in Microrobot Motion Control via Deep Reinforcement Learning and Path Planning Synergy

Advanced Intelligent Systems, EarlyView.
This study introduces a data‐driven framework that combines deep reinforcement learning with classical path planning to achieve adaptive microrobot navigation. By training a surrogate neural network to emulate microrobot dynamics, the approach improves learning efficiency, reduces training time, and enables robust real‐time obstacle avoidance in ...
Amar Salehi, Bairong Zhu, Haoyu Liu, Tingting Yu   +3 more
wiley   +1 more source

Variable Scheduling to Mitigate Channel Losses in Energy-Efficient Body Area Networks

Sensors, 2012
We consider a typical body area network (BAN) setting in which sensor nodes send data to a common hub regularly on a TDMA basis, as defined by the emerging IEEE 802.15.6 BAN standard.
Lavy Libman, Athanassios Boulis, Yuriy Tselishchev   +2 more
doaj   +1 more source

Labor Market Entry and Earnings Dynamics: Bayesian Inference Using Mixtures-of-Experts Markov Chain Clustering [PDF]

This paper analyzes patterns in the earnings development of young labor market entrants over their life cycle. We identify four distinctly different types of transition patterns between discrete earnings states in a large administrative data set. Further,
Andrea Weber, Christoph Pamminger, Rudolf Winter-Ebmer, Sylvia Frühwirth-Schnatter   +3 more
core  

Collaborative Multiagent Closed‐Loop Motion Planning for Multimanipulator Systems

Advanced Intelligent Systems, EarlyView.
This work presents a hierarchical multi‐manipulator planner, emphasizing highly overlapping space. The proposed method leverages an enhanced Dynamic Movement Primitive based planner along with an improvised Multi‐Agent Reinforcement Learning approach to ensure regulatory and mediatory control while ensuring low‐level autonomy. Experiments across varied
Tian Xu, Siddharth Singh, Qing Chang
wiley   +1 more source

Investigation of the relationship between macro-economic variables and tax evasion using nonlinear approaches [PDF]

اقتصاد باثبات
The main purpose of present study is to investigate the relationship between macroeconomic variables and tax evasion using nonlinear approaches. . First of all, it was used Markov Switching Vector Autoregression method, statistics and information from ...
masoumeh motallebi, Mohammad Alizadeh
doaj   +1 more source

Spatial land-use inventory, modeling, and projection/Denver metropolitan area, with inputs from existing maps, airphotos, and LANDSAT imagery [PDF]

A landscape model was constructed with 34 land-use, physiographic, socioeconomic, and transportation maps. A simple Markov land-use trend model was constructed from observed rates of change and nonchange from photointerpreted 1963 and 1970 airphotos ...
Christenson, J. W., Miller, L. D., Tom, C.   +2 more
core   +1 more source

An ontological morphological phylogenetic framework for living and extinct ray‐finned fishes (Actinopterygii)

The Anatomical Record, EarlyView.
Abstract The ray‐finned fishes include one out of every two species of living vertebrates on Earth and have an abundant fossil record stretching 380 million years into the past. The division of systematic knowledge of ray‐finned fishes between paleontologists working on extinct animals and neontologists studying extant species has obscured the ...
Jack Stack
wiley   +1 more source

Comparison of multi-state Markov models for cancer progression with different procedures for parameters estimation. An application to breast cancer

Epidemiology, Biostatistics and Public Health, 2013
Background: the knowledge of sojourn time (the duration of the preclinical screen-detectable period) and screening test sensitivity is crucial for understanding the disease progression and the effectiveness of screening programmes.
Leonardo Ventura, Giulia Carreras, Donella Puliti, Eugenio Paci, Marco Zappa, Guido Miccinesi   +5 more
doaj   +1 more source

Risk‐aware safe reinforcement learning for control of stochastic linear systems

Asian Journal of Control, EarlyView.
Abstract This paper presents a risk‐aware safe reinforcement learning (RL) control design for stochastic discrete‐time linear systems. Rather than using a safety certifier to myopically intervene with the RL controller, a risk‐informed safe controller is also learned besides the RL controller, and the RL and safe controllers are combined together ...
Babak Esmaeili, Nariman Niknejad, Hamidreza Modares   +2 more
wiley   +1 more source