The Math Behind Kernel Density Estimation
Explore the foundations, concepts, and mathematics behind Kernel Density Estimation (KDE), a powerful tool in non-parametric statistics for estimating probability density functions.
Recent posts
Understanding Heart Rate Variability Through the Lens of the Coefficient of Variation in Health Monitoring
Discover the significance of heart rate variability (HRV) and how the coefficient of variation (CV) provides a more nuanced view of cardiovascular health.
A Comparison of Predictive Maintenance Algorithms: Classical vs. Machine Learning Approaches
Explore the differences between classical statistical models and machine learning algorithms in predictive maintenance, including their performance, accuracy, and scalability in industrial settings.
Estimating Uncertainty in Neural Networks Using Monte Carlo Dropout
This article discusses Monte Carlo dropout and how it is used to estimate uncertainty in multi-class neural network classification, covering methods such as entropy, variance, and predictive probabilities.
Handling Rare Labels in Categorical Variables in Machine Learning
Rare labels in categorical variables can cause significant issues in machine learning, such as overfitting. This article explains why rare labels can be problematic and provides examples on how to handle them.
Big Data for Climate Change Mitigation
Big data is revolutionizing climate science, enabling more accurate predictions and helping formulate effective mitigation strategies.
GIS-Based Forest Fire Hotspot Identification: A Comprehensive Approach Using Contributory Factors
A study using GIS-based techniques for forest fire hotspot identification and analysis, validated with contributory factors like population density, precipitation, elevation, and vegetation cover.
Understanding Asymmetric Confidence Intervals: Causes and Implications
Discover the reasons behind asymmetric confidence intervals in statistics and how they impact research interpretation.
Understanding Type I and Type II Errors in Statistical Testing: How to Minimize False Conclusions
Learn how to avoid false positives and false negatives in hypothesis testing by understanding Type I and Type II errors, their causes, and how to balance statistical power and sample size.
Understanding Polynomial Regression: Why It’s Still Linear Regression
Polynomial regression is a popular extension of linear regression that models nonlinear relationships between the response and explanatory variables. However, despite its name, polynomial regression remains a form of linear regression, as the response variable is still a linear combination of the...