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A list of all the posts and pages found on the site. For you robots out there, there is an XML version available for digesting as well.
Pages
Posts
Future Blog Post
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Statistics for ML #97 — Time Series Analysis: ARIMA, ACF, PACF
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Time Series Analysis: ARIMA, ACF, PACF
Statistics for ML #96 — Autoencoders & VAE
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Autoencoders & VAE
Statistics for ML #95 — Vanishing & Exploding Gradients
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Vanishing & Exploding Gradients
Statistics for ML #94 — Weight Initialization Strategies
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Weight Initialization Strategies
Statistics for ML #93 — Dropout as Regularization
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Dropout as Regularization
Statistics for ML #92 — Batch Normalization
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Batch Normalization
Statistics for ML #91 — Activation Functions
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Activation Functions
Statistics for ML #90 — Backpropagation & Chain Rule
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Backpropagation & Chain Rule
Statistics for ML #89 — Gradient Descent & Variants
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Gradient Descent & Variants
Statistics for ML #88 — Loss Functions in Deep Learning
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Loss Functions in Deep Learning
Statistics for ML #87 — Probabilistic Graphical Models
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Probabilistic Graphical Models
Statistics for ML #86 — Variational Inference
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Variational Inference
Statistics for ML #85 — Expectation-Maximization (EM) Algorithm
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Expectation-Maximization (EM) Algorithm
Statistics for ML #84 — Gaussian Processes
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Gaussian Processes
Statistics for ML #83 — Markov Chain Monte Carlo (MCMC)
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Markov Chain Monte Carlo (MCMC)
Statistics for ML #82 — Hidden Markov Models (HMM)
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Hidden Markov Models (HMM)
Statistics for ML #81 — Markov Chains
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Markov Chains
Statistics for ML #80 — Naive Bayes Classifier
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Naive Bayes Classifier
Statistics for ML #79 — Bayesian Inference in ML
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Bayesian Inference in ML
Statistics for ML #78 — Imbalanced Data — SMOTE & Class Weights
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Imbalanced Data — SMOTE & Class Weights
Statistics for ML #77 — Calibration & Probability Scoring
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Calibration & Probability Scoring
Statistics for ML #76 — Precision, Recall, F1-Score
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Precision, Recall, F1-Score
Statistics for ML #75 — ROC Curve & AUC
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ROC Curve & AUC
Statistics for ML #74 — Gini Impurity
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Gini Impurity
Statistics for ML #73 — Entropy & Information Gain
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Entropy & Information Gain
Statistics for ML #72 — Factor Analysis
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Factor Analysis
Statistics for ML #100 — A/B Testing & Experimentation Design
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A/B Testing & Experimentation Design
Statistics for ML #71 — Singular Value Decomposition (SVD)
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Singular Value Decomposition (SVD)
Statistics for ML #99 — Causal Inference: DAGs, Do-Calculus
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Causal Inference: DAGs, Do-Calculus
Statistics for ML #98 — Survival Analysis & Hazard Functions
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Survival Analysis & Hazard Functions
Statistics for ML #57 — Multicollinearity & VIF
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Multicollinearity & VIF
Statistics for ML #56 — Residual Analysis & Diagnostics
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Residual Analysis & Diagnostics
Statistics for ML #55 — R-squared & Adjusted R-squared
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R-squared & Adjusted R-squared
Statistics for ML #54 — Gauss-Markov Theorem & BLUE
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Gauss-Markov Theorem & BLUE
Statistics for ML #53 — OLS Estimation
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OLS Estimation
Statistics for ML #52 — Multiple Linear Regression
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Multiple Linear Regression
Statistics for ML #51 — Simple Linear Regression
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Simple Linear Regression
Statistics for ML #70 — Principal Component Analysis (PCA)
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Principal Component Analysis (PCA)
Statistics for ML #69 — Feature Selection Methods
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Feature Selection Methods
Statistics for ML #68 — Bootstrap & Bagging
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Bootstrap & Bagging
Statistics for ML #67 — Cross-Validation: k-Fold, LOOCV
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Cross-Validation: k-Fold, LOOCV
Statistics for ML #66 — Train-Validation-Test Split
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Train-Validation-Test Split
Statistics for ML #65 — Overfitting & Underfitting
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Overfitting & Underfitting
Statistics for ML #64 — Bias-Variance Tradeoff
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Bias-Variance Tradeoff
Statistics for ML #63 — Polynomial & Nonlinear Regression
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Polynomial & Nonlinear Regression
Statistics for ML #62 — Ridge, Lasso & Elastic Net
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Ridge, Lasso & Elastic Net
Statistics for ML #61 — Poisson Regression
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Poisson Regression
Statistics for ML #60 — Logistic Regression & Log-Odds
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Logistic Regression & Log-Odds
Statistics for ML #59 — Autocorrelation & Durbin-Watson
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Autocorrelation & Durbin-Watson
Statistics for ML #58 — Heteroscedasticity & WLS
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Heteroscedasticity & WLS
Statistics for ML #50 — Multiple Testing & Bonferroni Correction
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Multiple Testing & Bonferroni Correction
Statistics for ML #49 — Non-parametric Tests
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Non-parametric Tests
Statistics for ML #48 — ANOVA: Analysis of Variance
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ANOVA: Analysis of Variance
Statistics for ML #47 — Chi-Square Test
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Chi-Square Test
Statistics for ML #46 — z-test & t-test
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z-test & t-test
Statistics for ML #45 — p-value & Statistical Significance
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p-value & Statistical Significance
Statistics for ML #44 — Type I & Type II Errors
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Type I & Type II Errors
Statistics for ML #43 — Hypothesis Testing Framework
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Hypothesis Testing Framework
Statistics for ML #42 — Conjugate Priors
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Conjugate Priors
Statistics for ML #41 — Bayesian Estimation & Posterior Distribution
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Bayesian Estimation & Posterior Distribution
Statistics for ML #40 — Method of Moments
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Method of Moments
Statistics for ML #39 — Maximum Likelihood Estimation (MLE)
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Maximum Likelihood Estimation (MLE)
Statistics for ML #38 — Properties of Estimators: Bias, Variance, Consistency
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Properties of Estimators: Bias, Variance, Consistency
Statistics for ML #37 — Confidence Intervals
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Confidence Intervals
Statistics for ML #36 — Point Estimation
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Point Estimation
Statistics for ML #35 — Log-Normal Distribution
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Log-Normal Distribution
Statistics for ML #34 — Multivariate Normal Distribution
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Multivariate Normal Distribution
Statistics for ML #33 — Dirichlet Distribution
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Dirichlet Distribution
Statistics for ML #32 — Beta Distribution
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Beta Distribution
Statistics for ML #31 — Exponential Distribution
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Exponential Distribution
Statistics for ML #30 — F-Distribution
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F-Distribution
Statistics for ML #29 — Chi-Square Distribution
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Chi-Square Distribution
Statistics for ML #28 — Student t-Distribution
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Student t-Distribution
Statistics for ML #27 — Standard Normal & Z-scores
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Standard Normal & Z-scores
Statistics for ML #26 — Normal (Gaussian) Distribution
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Normal (Gaussian) Distribution
Statistics for ML #25 — Uniform Distribution
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Uniform Distribution
Statistics for ML #24 — Geometric Distribution
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Geometric Distribution
Statistics for ML #23 — Poisson Distribution
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Poisson Distribution
Statistics for ML #22 — Binomial Distribution
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Binomial Distribution
Statistics for ML #21 — Bernoulli Distribution
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Bernoulli Distribution
Statistics for ML #20 — Moments of a Distribution
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Moments of a Distribution
Statistics for ML #19 — Degrees of Freedom
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Degrees of Freedom
Statistics for ML #18 — Standard Error
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Standard Error
Statistics for ML #17 — Sampling & Sampling Distributions
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Sampling & Sampling Distributions
Statistics for ML #16 — Central Limit Theorem (CLT)
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Central Limit Theorem (CLT)
Spatial Inequalities in Maternal Health: What Maps Tell Us That Tables Cannot
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Numbers in a regression table tell you that an association exists. A map tells you where it exists — and where it is worst. In maternal health research across South Asia and Sub-Saharan Africa, this geographic dimension is not just interesting; it is essential for policy.
Statistics for ML #15 — Law of Large Numbers
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Law of Large Numbers
Statistics for ML #14 — Expected Value & Variance
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Expected Value & Variance
Statistics for ML #13 — Joint, Marginal & Conditional Distributions
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Joint, Marginal & Conditional Distributions
Statistics for ML #12 — CDF: Cumulative Distribution Function
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CDF: Cumulative Distribution Function
Statistics for ML #11 — PDF: Probability Density Function
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PDF: Probability Density Function
Statistics for ML #9 — Random Variables
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A random variable is a function that maps outcomes of a random experiment to numbers. It is the bridge between probability theory and data.
Statistics for ML #8 — Bayes Theorem
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Bayes’ Theorem is the mathematical foundation of rational belief update. It is arguably the most important equation in statistics and modern ML.
Statistics for ML #7 — Conditional Probability
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Conditional probability is the probability of an event given that another event has occurred. It is perhaps the single most important concept in applied statistics and ML.
Statistics for ML #6 — Probability Axioms & Rules
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Probability is the mathematical language of uncertainty. Every ML model — from logistic regression to deep neural networks — is built on probability theory.
Statistics for ML #5 — Covariance & Correlation
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Covariance and correlation measure the linear relationship between two variables — the foundation of regression, PCA, and feature selection.
Statistics for ML #4 — Skewness & Kurtosis
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Mean and variance describe location and spread. Skewness and kurtosis describe the shape of a distribution — critical for choosing the right model.
Statistics for ML #3 — Measures of Dispersion
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Two datasets can have identical means but completely different spreads. Dispersion measures capture how spread out values are around the center.
Statistics for ML #2 — Measures of Central Tendency
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A measure of central tendency summarises an entire distribution with a single representative value. Choosing the wrong one can completely mislead your analysis.
Statistics for ML #1 — Types of Data: Nominal, Ordinal, Interval, Ratio
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Understanding data types is the most fundamental step before any analysis. Choosing the wrong statistical test or ML algorithm because you misidentified your data type is one of the most common mistakes in practice.
Statistics for ML — Complete 100-Post Series Index
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By Md Salek Miah — Statistician & ML Researcher | SUST, Bangladesh | saleksta@gmail.com
Teaching statistics for ML to researchers, epidemiologists, and data scientists — from first principles to advanced methods.
Machine Learning in Public Health: Why Explainability Matters
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In recent years, machine learning (ML) has transformed how researchers analyze complex health data. From predicting skilled birth attendance to classifying air quality, algorithms like XGBoost and Random Forest now routinely outperform traditional statistical models. But in public health, accuracy alone is not enough — we need to understand why a model makes a prediction.
Blog Post number 4
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This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
Blog Post number 3
Published:
This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
Blog Post number 2
Published:
This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
Blog Post number 1
Published:
This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
portfolio
Portfolio item number 1
Short description of portfolio item number 1
Portfolio item number 2
Short description of portfolio item number 2 
publications
Paper Title Number 1
Published in Journal 1, 2009
This paper is about the number 1. The number 2 is left for future work.
Recommended citation: Your Name, You. (2009). "Paper Title Number 1." Journal 1. 1(1).
Download Paper | Download Slides | Download Bibtex
Paper Title Number 2
Published in Journal 1, 2010
This paper is about the number 2. The number 3 is left for future work.
Recommended citation: Your Name, You. (2010). "Paper Title Number 2." Journal 1. 1(2).
Download Paper | Download Slides
Paper Title Number 3
Published in Journal 1, 2015
This paper is about the number 3. The number 4 is left for future work.
Recommended citation: Your Name, You. (2015). "Paper Title Number 3." Journal 1. 1(3).
Download Paper | Download Slides
Paper Title Number 4
Published in GitHub Journal of Bugs, 2024
This paper is about fixing template issue #693.
Recommended citation: Your Name, You. (2024). "Paper Title Number 3." GitHub Journal of Bugs. 1(3).
Download Paper
Paper Title Number 5, with math \(E=mc^2\)
Published in GitHub Journal of Bugs, 2024
This paper is about a famous math equation, \(E=mc^2\)
Recommended citation: Your Name, You. (2024). "Paper Title Number 3." GitHub Journal of Bugs. 1(3).
Download Paper
talks
Talk 1 on Relevant Topic in Your Field
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This is a description of your talk, which is a markdown file that can be all markdown-ified like any other post. Yay markdown!
Conference Proceeding talk 3 on Relevant Topic in Your Field
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This is a description of your conference proceedings talk, note the different field in type. You can put anything in this field.
teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
This is a description of a teaching experience. You can use markdown like any other post.
Teaching experience 2
Workshop, University 1, Department, 2015
This is a description of a teaching experience. You can use markdown like any other post.
