The Applied Probability Journals welcome papers on a wide variety of topics under the umbrella of Applied Probability. The interests of our editorial board represent this well (see here.) However, as a journal, we wanted to indicate some 'hot topic' themes below which we would particularly like to see submitted to the journals.
You may also wish to see collections of recently published topics in the Journal of Applied Probability and Advances in Applied Probability found on their respective journals pages. We have linked recent collections in relevent sections below.
Spatial and Spatio-Temporal Models
Spatial and spatio-temporal models analyse patterns and dynamics across geographical spaces or over time. These models find applications in environmental science, epidemiology, geostatistics, and urban planning, aiding in understanding complex interactions and phenomena.
Random Graphs and Network Science
Random graphs and network science, within applied probability, focus on probabilistic models to study complex networks' structures and behaviours. This field spans social networks, biological systems, the internet, and infrastructure analysis, uncovering emergent properties and vulnerabilities.
Statistical Physics and Stochastic Systems
Statistical Physics and Stochastic Systems delve into probabilistic models to understand collective behavior, phase transitions, and emergent properties in systems with numerous interacting components, impacting fields like materials science and complex dynamical systems.
Epidemiological models, utilize probabilistic frameworks to simulate disease spread, predict outbreaks, and evaluate interventions. These models aid public health decision-making by analysing contagion dynamics, vaccination strategies, and pandemic scenarios.
Finance and Actuarial Models
Finance and actuarial models in applied probability involve employing probabilistic methodologies to analyse and manage risks in financial markets, insurance, and investments. These models aid in pricing assets, assessing liabilities, and optimizing risk management strategies for businesses.
Survival analysis in applied probability explores time-to-event data, examining lifespans or durations until specific events. Widely used in medicine, sociology, and engineering, it predicts outcomes, estimates risks, and evaluates the impact of different factors on event occurrence.
Random matrices, pivotal in applied probability research, study the properties of matrices with random entries. Their applications span wireless communications, signal processing, quantum mechanics, and information theory, uncovering crucial insights into complex systems and phenomena.
Queueing theory, a fundamental aspect of applied probability research, analyses waiting lines and service systems' behavior. This field finds applications in telecommunications, healthcare, transportation, and computer systems, optimizing efficiency and performance in various real-world scenarios.
Algorithmic Game Theory
Algorithmic Game Theory merges game theory with probability to study strategic interactions in complex systems. This interdisciplinary field, in applied probability research, explores decision-making, incentives, and behavior of multiple agents, crucial in designing efficient and fair systems.
Risk Assessment and Security in Cyber-Physical Systems:
Applying probability models in engineering, environmental science, and various fields ensures safety and reliability in complex systems. Emphasis lies on using these models to bolster the security and dependability of interconnected systems, smart grids, and cyber-physical infrastructures.
Machine Learning and Probabilistic Models
Machine Learning and Probabilistic Models combine statistical inference with learning algorithms. These models, including Bayesian methods and probabilistic graphical models, enhance predictions, uncertainties estimation, and decision-making across diverse applications in various domains.
Markov Chain Monte Carlo (MCMC) Methods
Markov Chain Monte Carlo methods, pivotal in applied probability research, employ stochastic processes to simulate complex systems and sample from intricate probability distributions. Widely used in statistics and machine learning, MCMC facilitates inference and estimation in diverse fields.
Extreme Value Theory
Extreme Value Theory examines rare and extreme events in probabilistic models. It focuses on analysing the tail behavior of distributions, crucial in risk management, finance, environmental science, and understanding rare occurrences' impact on systems.
Markov Decision Processes
Markov Decision Processes (MDPs) model decision-making in stochastic environments. Essential in operations research and reinforcement learning, MDPs determine optimal strategies in dynamic systems, addressing uncertainty to make decisions across various fields like robotics and finance.
Bayesian Nonparametrics develops flexible probabilistic models without fixed parameters. These methods adapt to complex data structures, finding applications in diverse fields such as natural language processing, genetics, and computer vision for robust inference and prediction.