Information Theory, Probability and Statistics

In 1948 C. E. Shannon published his paper “A Mathematical Theory of Communication” in the Bell Systems Technical Journal. He showed how information could be quantified with absolute precision, and demonstrated the essential unity of all information media. In brief, he introduced four groundbreaking concepts that were influential enough to help change the world. Thus, his most eminent result was the concept that every communication channel had a speed limit, measured in binary digits per second. Additionally, he also realized that the content of the message was irrelevant to its transmission, since once data is represented digitally it could be regenerated and transmitted without error. On the other hand, the efficient representation of data, i.e. the source coding, was another question that Shannon opened for discussion. Finally, his paper also defined the amount of information that can be sent down a noisy channel in terms of transmit power and bandwidth, thus introducing the concept of entropy.

From that moment, this theory has been widely applied to numerous scenarios, such as statistical inference, natural language processing, cryptography, neurobiology, molecular engineering, ecology, medical physics, biomedical engineering, thermal physics, quantum computing, linguistics, plagiarism detection, pattern recognition and anomaly detection, among others. Indeed, in recent decades, it has played a key role in the invention of the compact disc, the feasibility of mobile phones, the development of the Internet, the study of linguistics and of human perception, the understanding of black holes, as well as in numerous other fields.

This section, focuses on original and new research results regarding this broad and deep mathematical theory, as well as in diverse applications. Thus, manuscripts on source coding, channel coding, algorithmic complexity theory, algorithmic information theory, information–theoretic security, and measures of information, as well as on their application to traditional as well as novel scenarios are solicited. Submissions addressing critical up-to-date reviews will also be welcome. Download Section Flyer  

Prof. Dr. Raúl Alcaraz Martínez
Section Editor-in-Chief

Information Theory:

• communications and communications networks;
• sequences;
• coding theory and techniques;
• network coding and lattice theory;
• quantum information theory;
• signal processing;
• Shannon theory;
• complexity and cryptography;
• data compression;
• multi-user, multi-variate and hyper dimensional information theory;
• coded modulation;
• computational complexity;
• information dynamics and measures ;
• theoretical computer science and artificial intelligence;
• information theoretic learning;
• information fusion;
• fractional order generalized information;
• emerging applications of information theory in economics, medicine, biology, industry, thermodynamics, education, chemistry, physics, cognitive science, and social science;
• application of information theory in wireless/multimedia applications;
• application of information theory in image processing, computer graphics and visualization

Statistics:

• machine learning and its applications;
• deep learning and its applications;
• learning and inference;
• pattern recognition;
• statistical learning and data mining;
• information algebra/geometry;
• stochastic processes;
• computational statistics;
• statistical modeling;
• natural language processing;
• emerging applications of statistical theory in economics, medicine, biology, industry, thermodynamics, education, chemistry, physics, cognitive science,and social science

Probability:

• detection and estimation theory;
• probability theory;
• communication combinatorial problems;
• decision-making theory;
• emerging applications of probability theory in economics, medicine, biology, industry, thermodynamics, education, chemistry, physics, cognitive science, and social science