Md Salek Miah

Md Salek Miah

he/him

Statistician & ML Specialist | Epidemiology & Public Health | 2 Published + 20+ Under Review in Q1 Journals | PhD Applicant

Posts by Tags

Bangladesh

Spatial Inequalities in Maternal Health: What Maps Tell Us That Tables Cannot

2 minute read

Published:

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.

EDA

Statistics for ML #4 — Skewness & Kurtosis

2 minute read

Published:

Mean and variance describe location and spread. Skewness and kurtosis describe the shape of a distribution — critical for choosing the right model.

GIS

Spatial Inequalities in Maternal Health: What Maps Tell Us That Tables Cannot

2 minute read

Published:

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.

Nepal

Spatial Inequalities in Maternal Health: What Maps Tell Us That Tables Cannot

2 minute read

Published:

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.

SHAP

Machine Learning in Public Health: Why Explainability Matters

1 minute read

Published:

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.

bayesian-ml

category1

Future Blog Post

less than 1 minute read

Published:

This post will show up by default. To disable scheduling of future posts, edit config.yml and set future: false.

Blog Post number 4

less than 1 minute read

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 3

less than 1 minute read

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

less than 1 minute read

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

less than 1 minute read

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.

category2

Future Blog Post

less than 1 minute read

Published:

This post will show up by default. To disable scheduling of future posts, edit config.yml and set future: false.

Blog Post number 4

less than 1 minute read

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 3

less than 1 minute read

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

less than 1 minute read

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

less than 1 minute read

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.

causal-inference

cool posts

Future Blog Post

less than 1 minute read

Published:

This post will show up by default. To disable scheduling of future posts, edit config.yml and set future: false.

Blog Post number 4

less than 1 minute read

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 3

less than 1 minute read

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

less than 1 minute read

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

less than 1 minute read

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.

data-types

deep-learning

descriptive-statistics

dimensionality-reduction

distribution-shape

Statistics for ML #4 — Skewness & Kurtosis

2 minute read

Published:

Mean and variance describe location and spread. Skewness and kurtosis describe the shape of a distribution — critical for choosing the right model.

distributions

epidemiology

Spatial Inequalities in Maternal Health: What Maps Tell Us That Tables Cannot

2 minute read

Published:

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.

Machine Learning in Public Health: Why Explainability Matters

1 minute read

Published:

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.

experimentation

explainable AI

Machine Learning in Public Health: Why Explainability Matters

1 minute read

Published:

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.

foundations

Statistics for ML #9 — Random Variables

1 minute read

Published:

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

1 minute read

Published:

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

1 minute read

Published:

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 #4 — Skewness & Kurtosis

2 minute read

Published:

Mean and variance describe location and spread. Skewness and kurtosis describe the shape of a distribution — critical for choosing the right model.

inference

machine learning

Machine Learning in Public Health: Why Explainability Matters

1 minute read

Published:

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.

machine-learning

Statistics for ML #9 — Random Variables

1 minute read

Published:

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

1 minute read

Published:

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

1 minute read

Published:

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.

maternal health

Spatial Inequalities in Maternal Health: What Maps Tell Us That Tables Cannot

2 minute read

Published:

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.

model-evaluation

neural-networks

optimization

probabilistic-models

probability

public health

Machine Learning in Public Health: Why Explainability Matters

1 minute read

Published:

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.

public-health

regression

series-index

spatial analysis

Spatial Inequalities in Maternal Health: What Maps Tell Us That Tables Cannot

2 minute read

Published:

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.

standard-deviation

statistics

Statistics for ML #9 — Random Variables

1 minute read

Published:

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

1 minute read

Published:

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

1 minute read

Published:

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 #4 — Skewness & Kurtosis

2 minute read

Published:

Mean and variance describe location and spread. Skewness and kurtosis describe the shape of a distribution — critical for choosing the right model.

survival-analysis

time-series

variance