State Space Models (SSMs) offer a versatile framework for time series analysis, especially in dynamic systems. This article explores discretization, the Kalman filter, and Bayesian approaches, including their use in econometrics.
Explore how machine learning can be leveraged to forecast commodity prices, such as oil and gold, using advanced predictive models and economic indicators.
The integration of IoT and big data is revolutionizing elderly care by enabling remote monitoring systems that track vital signs, detect emergencies, and ensure quick responses to health risks.
Outliers, or extreme observations in datasets, can have a significant impact on statistical analysis. Learn how to detect, analyze, and manage outliers effectively to ensure robust data analysis.
The rich are getting richer while the poor remain poor. This article dives into the physics-based models that explain the inherent inequality in wealth distribution.
Optimal control theory, employing Hamiltonian and Lagrangian methods, offers powerful tools in modeling and optimizing fiscal and monetary policy.
This article critically examines the use of Bayesian posterior distributions as test statistics, highlighting the challenges and implications.
The Liquid State Machine offers a unique framework for computations within biological neural networks and adaptive artificial intelligence. Explore its fundamentals, theoretical background, and practical applications.
Grubbs’ test is a statistical method used to detect outliers in a univariate dataset, assuming the data follows a normal distribution. This article explores its mechanics, usage, and applications.
Capture-Mark-Recapture (CMR) is a powerful statistical method for estimating wildlife populations, relying on six key assumptions for reliability.