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Everything about Histogram
Graphical representation of the distribution of numerical data
A histogram is a visual representation of the distribution of quantitative data. To construct a histogram, the first step is to "bin" the range of values— divide the entire range of values into a series of intervals—and then count how many values fall into each interval. The bins are usually specified as consecutive, non-overlapping intervals of a variable. The bins (intervals) are adjacent and are typically of equal size. Wikipedia →
🧭 Start here: a 6-step reading path
- Read the overview: HistogramA 5-minute orientation
- Skim a recent paper: A machine learning-based framework for online prediction of battery ageing trajectory and lifetime using histogram dataInfluential work since 2022
- Pick up a classic book: The effects of cloud inhomogeneities upon radiative fluxes, and the supply of a cloud truth validation datasetThe most-reprinted book on the subject
- Read the landmark paper: THE weighted histogram analysis method for free‐energy calculations on biomolecules. I. The methodCited 6,708 times
- Go sideways: Freedman–Diaconis ruleA closely related topic
- Go sideways: Frequency (statistics)A closely related topic
📖 Definitions
Noun A graphical display of numerical data in the form of upright bars, with the area of each bar representing frequency.
Verb To represent (data) as a histogram.
🗂️ The subject at a glance
🏆 Landmark papers (most cited)
THE weighted histogram analysis method for free‐energy calculations on biomolecules. I. The method
Adaptive histogram equalization and its variations
A new method for gray-level picture thresholding using the entropy of the histogram
The vector field histogram-fast obstacle avoidance for mobile robots
g_wham—A Free Weighted Histogram Analysis Implementation Including Robust Error and Autocorrelation Estimates
Realization of the Contrast Limited Adaptive Histogram Equalization (CLAHE) for Real-Time Image Enhancement
On the histogram as a density estimator:L 2 theory
Support vector machines for histogram-based image classification
🆕 Influential since 2022
A machine learning-based framework for online prediction of battery ageing trajectory and lifetime using histogram data
Comparison of histogram-based gradient boosting classification machine, random Forest, and deep convolutional neural network for pavement raveling severity classification
Automated bone marrow cytology using deep learning to generate a histogram of cell types
Histogram of the orientation of the weighted phase descriptor for multi-modal remote sensing image matching
Detection and classification of COVID-19 disease from X-ray images using convolutional neural networks and histogram of oriented gradients
📚 Books worth reading
- 📕The effects of cloud inhomogeneities upon radiative fluxes, and the supply of a cloud truth validation datasetRonald M. Welch · 1992
- 📕Comparison tools for assessing the microgravity environment of orbital missions, carriers and conditionsRichard DeLombard · 1999
Paint Shop Pro 9 and Studio in Easy StepsStephen Copestake · 2005
Ranked by number of editions on Open Library, a rough measure of how lasting a book is.
🔗 Related topics
- Freedman–Diaconis rule In statistics, the Freedman–Diaconis rule can be used to select the width of the bins to be used in a histogram. It is named after David A. Freedman and…
- Frequency (statistics) In statistics, the frequency or absolute frequency of an event i {\displaystyle i} is the number n i {\displaystyle n_{i}} of times the observation has…
- Scott's rule Scott's rule is a method to select the number of bins in a histogram. Scott's rule is widely employed in data analysis software including R, Python and…
- Multivariate kernel density estimation Kernel density estimation is a nonparametric technique for density estimation i.e., estimation of probability density functions, which is one of the fundamental…
- Sturges's rule Sturges's rule is a method to choose the number of bins for a histogram. Given n {\displaystyle n} observations, Sturges's rule suggests using k ^ = 1…
- Kernel density estimation In statistics, kernel density estimation (KDE) is the application of kernel smoothing for probability density estimation, i.e., a non-parametric method…
- Glossary of probability and statistics These terms and concepts are used in the mathematical sciences of statistics and probability, their sub-disciplines, and related fields. For additional…
- Mode (statistics) In statistics, the mode is the value that appears most often in a set of data values. If X is a discrete random variable, the mode is the value x at which…