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MetodeStatistikk1,836 KI og maskinlæring1,661 Beslutningsvitenskap932 Forskningsmetoder1,354 Måling1,745 Kausalitet og evidens532 Forskningspraksis118

1,522 metoder · KI og maskinlæringTøm

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signal processing

Blind Source Separation

Blind Source Separation (BSS) is a signal processing technique that recovers original signals from their unknown mixture without detailed knowledge of the mixing process. Through the framework of Independent Component Analysis (ICA), BSS recovers statistically independent source signals using only the assumption that s

2 kilder1994

computer vision

Blob Detection

Blob detection is a technique for identifying regions of interest (blobs)—connected, homogeneous areas that differ from their surroundings—at multiple scales. Introduced by Lindeberg in the context of scale-space theory, blob detection automatically finds and characterizes circular or elliptical objects without requiri

2 kilder1998

cryptography

Blockchain Consensus

Blockchain consensus mechanisms are distributed protocols that enable a network of untrusted nodes to agree on the correct state of a ledger without a central authority. Introduced with Bitcoin in 2008, consensus mechanisms like Proof of Work and Proof of Stake ensure that modifications to the blockchain cannot be made

2 kilder2008

mining engineering

Bond Work Index

The Bond Work Index, introduced by Fred C. Bond in 1952, is an empirical parameter that characterizes the resistance of an ore to grinding in a tumbling mill. It is defined as the kilowatt-hours per short ton (kWh/st) of electrical energy required to reduce a coarse ore from theoretically infinite size to 80% passing 1

2 kilder1952

machine learning

Boosting

Boosting is a sequential ensemble technique that converts many simple, barely-better-than-chance learners into a single highly accurate model by repeatedly focusing training on the examples that previous learners got wrong, then combining all learners with weights proportional to their individual accuracy.

2 kilder1990

ensemble learning

Boosting Ensemble

Boosting is an ensemble method that sequentially trains weak learners and combines them into a strong predictor by focusing on samples that previous models misclassified. Each new weak learner is weighted according to the difficulty of its training task, and final predictions are made via weighted voting. Pioneered by

2 kilder1990

ensemble learning

Borda Count Aggregation

Borda count is a preference aggregation method that combines ranked predictions from multiple classifiers by assigning points based on ranking position. Each classifier ranks the possible outcomes, and each class receives points inversely proportional to its rank position. The class with the highest total score is sele

2 kilder1781

quantum computing

Born-Oppenheimer Approximation

The Born-Oppenheimer (BO) Approximation is a foundational assumption in molecular quantum mechanics that nuclei can be treated as fixed while solving for electrons, and vice versa. Introduced by Born and Oppenheimer in 1927, this separation reduces the complex many-body electronic-nuclear problem to a sequence of simpl

3 kilder1927

materials science

Boundary Element Method

The Boundary Element Method (BEM) is a numerical technique that solves partial differential equations by transforming them into boundary integral equations, requiring discretization only of the problem boundary rather than the entire domain. Developed systematically by Carlos Brebbia in the late 1970s, BEM offers signi

3 kilder1978

fluid dynamics

Boundary Layer Theory

Boundary Layer Theory is the analytical and approximate framework for understanding viscous flow near solid surfaces, pioneered by Ludwig Prandtl in 1904. The central insight is that at high Reynolds numbers, viscous effects are confined to a thin layer near walls (the boundary layer), while the flow outside remains es

3 kilder1904

thermodynamics

Boussinesq Approximation

The Boussinesq Approximation simplifies the governing equations for natural convection by treating density as constant except in the buoyancy term. This approximation is valid when temperature variations produce small density changes and allows researchers to solve coupled heat-fluid flow problems without solving the f

2 kilder1903

thermodynamics

Brayton Cycle

The Brayton Cycle (also called Joule Cycle) describes the thermodynamic process in gas turbines and jet engines. It consists of four processes: isentropic compression in a compressor, isobaric combustion (heat addition), isentropic expansion in a turbine, and isobaric heat rejection. The Brayton Cycle is the foundation

2 kilder1873

model evaluation

Brier Score

The Brier score measures the mean squared difference between predicted probabilities and actual binary outcomes. It is a simple, interpretable metric for evaluating the accuracy of probabilistic predictions, particularly in weather forecasting and medical diagnosis.

2 kilder1950

clinical psychology

BSQ

The BSQ is a self-report questionnaire measuring preoccupation with and dissatisfaction about body shape. Originally developed by Cooper and colleagues in 1987, the full version contains 34 items; shorter versions (BSQ-16, BSQ-8) are also widely used. The BSQ was designed to assess body shape concern as a core psychopa

3 kilder1987

architecture

Building Energy Performance Simulation

Building Energy Performance Simulation is a computational method for predicting how much energy a building consumes for heating, cooling, lighting, and equipment operation under specified weather and occupancy conditions. Pioneered by researchers like Joe Clarke and Drury Crawley in the 1990s, it has become essential f

3 kilder1993

meteorology

Bulk Aerodynamic Flux

The bulk aerodynamic method estimates surface energy and momentum fluxes from standard meteorological observations. Rather than measuring turbulent fluxes directly, it parameterizes them using measurements of wind speed, temperature, and moisture at a reference height (typically 10 m) and surface conditions, multiplied

2 kilder1981

model evaluation

Calinski-Harabasz Index

The Calinski-Harabasz Index, also called the Variance Ratio Criterion, was introduced by Calinski and Harabasz in 1974. It is a metric that measures the ratio of between-cluster variance to within-cluster variance, adjusted for the number of clusters and data points. Higher values indicate better-separated, more compac

1 kilde1974

particle physics

Calorimeter Calibration

Calorimeter calibration establishes the relationship between the measured energy deposited in a detector and the true energy of incident particles. Precise calibration is essential for physics measurements, Higgs boson properties, and new physics searches at colliders, requiring careful control of systematic uncertaint

3 kilder1990

materials science

CALPHAD

CALPHAD (CALculation of PHAse Diagrams) is a computational method for predicting thermodynamic equilibrium properties and phase diagrams of multicomponent alloys. Pioneered by Larry Kaufman in 1970, CALPHAD combines experimental and computational data to assess thermodynamic properties of phases and subsequently predic

3 kilder1970

computer vision

Canny Edge Detection

The Canny edge detector, introduced by John Canny in 1986, is a multi-stage algorithm for identifying edges in digital images where significant intensity changes occur. Canny's method is optimal for step edges in additive Gaussian noise and remains the gold standard for edge detection in computer vision due to its math

2 kilder1986

deep learning

Capsule Network

A Capsule Network (CapsNet) is a deep learning architecture introduced by Sara Sabour, Nicholas Frosst and Geoffrey Hinton in 2017 that organises neurons as vectors (capsules) rather than scalar activations, so that spatial hierarchy and pose (orientation) information are encoded directly. It was proposed to overcome t

2 kilder2017

quantitative finance

Carr-Madan FFT

The Carr-Madan Fast Fourier Transform (1999) is a highly efficient method for computing option prices across a range of strikes using characteristic functions and FFT. It enables rapid pricing of European options under any model with a known characteristic function (Heston, Merton jumps, Variance Gamma), with computati

2 kilder1999

machine learning

CatBoost

CatBoost is a gradient boosting algorithm, introduced by Prokhorenkova and colleagues at Yandex in 2018, that handles categorical variables natively and uses ordered target encoding to avoid label leakage. By building an additive ensemble of trees while permuting the data order at each iteration, it is often superior t

1 kilde2018

time series

CEEMDAN

Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) is an improved variant of empirical mode decomposition (EMD) that addresses mode-mixing artifacts through ensemble averaging with adaptive noise. Introduced by Torres and colleagues (2011), CEEMDAN decomposes signals into intrinsic mode functi

3 kilder2011

simulation

Cellular Automata

Cellular automata (CA) is a grid-based computational simulation model, first formalized by John von Neumann and Stanislaw Ulam in the 1940s–1950s and brought to wide attention by John Conway's Game of Life (1970) and Stephen Wolfram's systematic classification (2002), in which a lattice of cells — each holding a finite

2 kilder1940

network analysis

Centrality Analysis

Centrality analysis is a family of network-analytic measures, formalized by Freeman (1979), that quantifies the structural importance of individual nodes within a graph. Each centrality index captures a distinct mechanism of influence: degree centrality reflects direct connectivity, betweenness centrality identifies no

2 kilder1979

acoustics

Cepstral Analysis

Cepstral analysis is a spectral analysis technique that decomposes signals into independent components by inverting the log-magnitude spectrum. Pioneered by Bogert, Healy, and Tukey in 1963, cepstral analysis reveals periodic structure in spectra (pitch, echo patterns) and separates source excitation from filter respon

3 kilder1963

bioinformatics

ChIP-seq Peak Calling

ChIP-seq peak calling is a computational pipeline that identifies genomic regions where a protein of interest — a transcription factor or histone modification — is enriched, based on sequencing reads from chromatin immunoprecipitation experiments. It converts raw sequencing data into a set of high-confidence binding or

2 kilder2007

music information retrieval

Chord Recognition

Chord recognition is the task of automatically identifying the harmonic chords present in a musical recording and estimating when chord changes occur. Introduced formally by Harte et al. (2005), it is a cornerstone of music analysis and widely used in music education, cover song analysis, and musical structure understa

3 kilder2005

deep learning

Chronos

Chronos is a family of pre-trained probabilistic forecasting models introduced by Ansari et al. at Amazon in 2024. It adapts the language-model paradigm to time series by quantizing continuous values into discrete tokens, enabling a standard transformer to be trained on a large heterogeneous corpus of time-series data.

1 kilde2024

text mining

Chunking

Chunking, also called shallow parsing, is a natural-language-processing task introduced by Steven Abney in 1991 that divides text into grammatical pieces — such as noun phrases and verb phrases — using part-of-speech tags. It extracts useful syntactic structure quickly without building a full parse tree of the sentence

2 kilder1991

numerical methods

CI/CD Analytics

CI/CD Analytics is the measurement and analysis of Continuous Integration and Continuous Deployment pipelines to improve development velocity, quality, and reliability. Popularized by Humble and Farley's 'Continuous Delivery' (2010) and formalized by Forsgren et al.'s 'Accelerate' (2018), key metrics include deployment

3 kilder2010

numerical methods

Cleanroom Software Engineering

Cleanroom Software Engineering is a software development methodology developed by Mills, Dyer, and Linger in the 1980s that emphasizes defect prevention through formal specifications, code reviews, and statistical testing rather than debugging. Inspired by pharmaceutical manufacturing cleanrooms, the approach aims for

3 kilder1987

text mining

Clinical Text Mining

Clinical text mining is a specialised branch of natural language processing that extracts structured clinical facts — diagnoses, symptoms, medications, treatments, and ICD codes — from unstructured healthcare documents such as discharge summaries, progress notes, and radiology reports. Grounded in biomedical NLP models

2 kilder2000

deep learning

CLIP

CLIP (Contrastive Language-Image Pretraining) is a vision-language model introduced by Radford et al. at OpenAI in 2021 that jointly learns aligned image and text representations by training on 400 million internet-sourced image-text pairs using a contrastive objective, enabling zero-shot transfer to image classificati

3 kilder2021

network analysis

Closeness Centrality

Closeness centrality measures how quickly a node can reach all others in a network by computing the inverse of its average shortest-path distance to every other node. First described by Bavelas (1950) and formally unified by Freeman (1979), it identifies nodes that can spread information or resources efficiently across

2 kilder1950

meteorology

Cloud Condensation Nuclei Analysis

Cloud condensation nuclei (CCN) analysis examines the number and properties of aerosol particles capable of nucleating cloud droplets at various supersaturation levels. This field involves measuring CCN concentrations, characterizing their chemical composition and size, and relating aerosol properties to cloud microphy

2 kilder1959

astronomy

CMB Anisotropy Analysis

The Cosmic Microwave Background is the ancient light from when the universe first became transparent, about 380,000 years after the Big Bang. Its tiny temperature variations (anisotropies) across the sky encode a wealth of information about the universe's composition, geometry, and history. First discovered by Arno Pen

3 kilder1965

deep learning

CNN Image Classification

CNN image classification uses deep convolutional architectures such as ResNet (He et al., 2016), VGG and EfficientNet (Tan & Le, 2019) to sort images into categories. Stacked convolutional layers learn a hierarchy of visual features directly from pixels, and skip (residual) connections prevent the vanishing-gradient pr

2 kilder2016

text mining

Co-occurrence Analysis

Co-occurrence analysis is a text-mining technique that statistically counts the word pairs that appear together within a window or a sentence and uses their frequencies to reveal semantic maps and thematic structure. It rests on the distributional principle articulated by J.R. Firth in 1957 — that a word is characteris

2 kilder1957

oceanography

CO2SYS

CO2SYS is a widely-used software package for calculating the speciation and equilibrium state of the marine carbonate system from measurements of two carbonate parameters. Developed by Ernie Lewis and Doug Wallace in 1998, CO2SYS enables oceanographers to compute all carbonate species (dissolved CO2, bicarbonate, carbo

2 kilder1998

genetics

Coalescent Theory

Coalescent theory is a probabilistic framework that traces the genealogical history of DNA sequences backward in time to their most recent common ancestor. Developed by John Kingman in 1982, this method forms the foundation of modern population genetics, enabling researchers to understand demographic events, estimate g

3 kilder1982

machine learning

Collaborative Filtering

Collaborative filtering recommends items to a user by leveraging the preferences of many users — 'people who liked what you liked also liked this'. It learns from a sparse user-item interaction matrix, either by finding similar users or items (neighbourhood methods, formalized by Sarwar et al. in 2001) or by factorizin

2 kilder2001

text mining

Collocation Analysis

Collocation analysis is a statistical text-mining technique that identifies word pairs or expressions that frequently occur together, using association measures rather than chance co-occurrence. Introduced in the lexicography work of Church and Hanks (1990), it is used for terminology extraction and language analysis,

2 kilder1990

biomechanics

Common Spatial Pattern

Common Spatial Pattern (CSP) is a spatial filtering technique that identifies electrode combinations that maximize the variance difference between two classes of EEG activity, typically used in brain-computer interfaces to enhance motor imagery discrimination. Introduced by Ramoser and colleagues in 2000, CSP has becom

2 kilder2000

text mining

Commonsense Reasoning

Commonsense reasoning in NLP refers to the capacity of a language model or inference system to draw on implicit, world-knowledge facts that humans take for granted — facts not stated in the text — to answer questions, complete stories, or interpret dialogue. Landmark benchmarks formalising the task include ATOMIC (Sap

2 kilder2019

network analysis

Community Detection

Community detection is a family of graph-partitioning algorithms that discover densely connected sub-groups — communities — within a network. First formalised through the modularity measure by Girvan and Newman (2002), the field advanced rapidly with the Louvain method (Blondel et al., 2008), the Leiden refinement (Tra

2 kilder2002

linguistics

Comparative Method

The Comparative Method is a foundational technique in historical linguistics for reconstructing ancestral languages and establishing genetic relationships between related languages. Pioneered by Sir William Jones in 1786, it systematically compares phonological, morphological, and lexical features across languages to i

3 kilder1786

signal processing

Compressive Sensing

Compressive Sensing (CS) is a signal acquisition and reconstruction technique that exploits signal sparsity to recover high-resolution signals from far fewer samples than required by the Nyquist sampling theorem. Developed by Emmanuel Candès, Justin Romberg, and Terence Tao in 2006, compressive sensing challenges the t

2 kilder2006

psychometrics

Computerized adaptive test item analysis

Computerized adaptive test item analysis evaluates and calibrates items intended for use in adaptive testing environments. Unlike fixed-form analysis, it accounts for the non-random item exposure inherent in adaptive administration, using item response theory to estimate item parameters, information functions, and expo

2 kilder1970

model evaluation

Confusion Matrix

The confusion matrix is a table that displays the counts of true positives, true negatives, false positives, and false negatives. It provides a complete picture of where a classifier makes correct and incorrect predictions, enabling calculation of all other classification metrics.

2 kilder

numerical methods

Conjugate Gradient Method

The Conjugate Gradient (CG) Method is an iterative algorithm for solving large sparse symmetric positive-definite linear systems Ax = b, developed by Hestenes and Stiefel in 1952. It is one of the most widely used iterative solvers in scientific computing because it converges in at most n iterations for an n × n matrix

3 kilder1952

text mining

Constituency Parsing

Constituency parsing is a natural-language-processing task that represents a sentence as a tree of recursively nested phrase-structure constituents — for example S → NP + VP. Building on the head-driven statistical parsing models introduced by Collins (2003) and the later neural parsers of Kitaev and colleagues (2019),

2 kilder2003

computer vision

Contour Analysis

Contour analysis is the process of detecting and analyzing the boundaries of objects in images by identifying connected edges and extracting shape information. The Suzuki-Abe algorithm provides an efficient method for finding contours in binary images, enabling shape-based object classification and segmentation.

2 kilder1985

text mining

Contrastive Learning for NLP

Contrastive learning for NLP is a representation-learning technique — popularised by SimCSE (Gao et al., 2021) and Supervised Contrastive Learning (Khosla et al., 2020) — that trains a text encoder by pulling embeddings of similar text pairs together while pushing embeddings of dissimilar pairs apart. The result is a d

2 kilder2020

deep learning

Convolutional Neural Network

A Convolutional Neural Network (CNN) is a deep learning model, established by LeCun and colleagues in 1998, that learns local patterns directly from images and structured data to classify them. Stacks of convolutional filters discover increasingly abstract features, so manual feature engineering can be largely reduced.

1 kilde1998

bioinformatics

Copy Number Variation Analysis

Copy number variation (CNV) analysis is a genomic pipeline for detecting regions where individuals carry fewer or more copies of a DNA segment than the reference genome. CNVs span kilobases to megabases and are a major class of structural variation implicated in cancer, neurodevelopmental disorders, and population dive

2 kilder1998

text mining

Coreference Resolution

Coreference resolution is a natural-language-processing task that detects when different expressions in a text refer to the same entity — for example a name, a later pronoun, and a descriptive phrase all pointing at one person. Rooted in early linguistic work by Hobbs (1978) and advanced by the end-to-end neural model

2 kilder1978

linguistics

Corpus Linguistics

Corpus Linguistics is the study of language based on large, representative collections of texts (corpora) processed by computer. Pioneered by John Sinclair and others, the method uses statistical analysis, concordancing, and computational tools to examine patterns of actual language use. Corpus linguistics has transfor

3 kilder1980

applied physics

Cosmological Perturbation Theory

Cosmological perturbation theory describes how small density fluctuations in the early universe grow into galaxies, clusters, and large-scale structure under gravity. Originating from James Jeans's 1902 stability analysis and extended by Lifshitz, Bardeen, and others, this theory is the foundation of structure formatio

3 kilder1902

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