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Computational hardness assumption
The Decision Linear assumption (DLIN assumption) is a computational hardness assumption used in elliptic curve cryptography. In particular, the DLIN assumption
Decision_Linear_assumption
Assumption used in cryptographic systems
The decisional Diffie–Hellman (DDH) assumption is a computational hardness assumption about a certain problem involving discrete logarithms in cyclic
Decisional Diffie–Hellman assumption
Decisional_Diffie–Hellman_assumption
Privacy-based cryptographic primitive
on the Strong Diffie Hellman assumption (SDH) and a new assumption in bilinear groups called the Decision linear assumption (DLin). A more formal definition
Group_signature
Cryptographic primitive
Proof systems under the sub-group hiding, decisional linear assumption, and external Diffie–Hellman assumption that allow directly proving the pairing product
Non-interactive zero-knowledge proof
Non-interactive_zero-knowledge_proof
Public-key cryptographic pseudorandom function
secure given any member of the "(n − 1)-linear assumption family", which includes the decision linear assumption. This is the first such VRF constructed
Verifiable_random_function
Linear regression model with a single explanatory variable
In statistics, simple linear regression (SLR) is a linear regression model with a single explanatory variable. That is, it concerns two-dimensional sample
Simple_linear_regression
Measure of value difference between best possible decision and made decision
optimal, minimax regret-minimizing linear estimator, which can be seen by the following argument. According to the assumptions, the observed vector y {\displaystyle
Regret_(decision_theory)
Statistical modeling method
parameters of linear regression models with standard estimation techniques such as ordinary least squares, it is necessary to make a number of assumptions about
Linear_regression
Mathematical model for sequential decision making under uncertainty
When this assumption is not true, the problem is called a partially observable Markov decision process or POMDP. Constrained Markov decision processes
Markov_decision_process
Method used in statistics, pattern recognition, and other fields
assumption of the LDA method. LDA is also closely related to principal component analysis (PCA) and factor analysis in that they both look for linear
Linear_discriminant_analysis
System of resource-aware logic
Linear logic is a substructural logic proposed by French logician Jean-Yves Girard as a refinement of classical and intuitionistic logic, joining the dualities
Linear_logic
Branch of applied probability theory
make better decisions. In contrast, descriptive decision theory is concerned with describing observed behaviors often under the assumption that those making
Decision_theory
Amount in information economics
members, i.e., there might not exist linear ordering of decisions and uncertainties satisfying perfect recall assumption. VoC thus captures the value of being
Value_of_information
Class of statistical models
generalized linear model (GLM) is a flexible generalization of ordinary linear regression. The GLM generalizes linear regression by allowing the linear model
Generalized_linear_model
Statistical linear model
multivariate normal distribution, generalized linear models may be used to relax assumptions about Y and U. The general linear model (GLM) encompasses several statistical
General_linear_model
Statistical classification in machine learning
In machine learning, a linear classifier makes a classification decision for each object based on a linear combination of its features. A simpler definition
Linear_classifier
Set of statistical processes for estimating the relationships among variables
assumptions do not hold exactly. In linear regression, the model specification is that the dependent variable, y i {\displaystyle y_{i}} is a linear combination
Regression_analysis
Mathematical optimization problem restricted to integers
problems. If some decision variables are not discrete, the problem is known as a mixed-integer programming problem. Integer linear programs can be expressed
Integer_programming
Statistical classifier in machine learning
quadratic decision surface to separate measurements of two or more classes of objects or events. It is a more general version of the linear classifier
Quadratic_classifier
Decision-making process used by consumers
rationality assumptions of homo economicus with a conception of rationality tailored to cognitively limited agents. Even if the buyer decision process was
Buyer_decision_process
Process of using data analysis for predicting population data from sample data
population depends in a truly linear manner on some covariate (a parametric assumption) but not make any parametric assumption describing the variance around
Statistical_inference
Machine learning algorithm
human decision making more closely than other approaches. This could be useful when modeling human decisions/behavior. Robust against co-linearity, particularly
Decision_tree_learning
General linear model that blends ANOVA and regression
several key assumptions that underlie the use of ANCOVA and affect interpretation of the results. The standard linear regression assumptions hold; further
Analysis_of_covariance
Approximation method in statistics
linear or ordinary least squares and nonlinear least squares, depending on whether or not the model functions are linear in all unknowns. The linear least-squares
Least_squares
Cost accounting model
same basic assumptions as in breakeven analysis. The assumptions underlying CVP analysis are: The behavior of both costs and revenues is linear throughout
Cost–volume–profit_analysis
In marketing, a type of hierarchy of effects model
steps or stages when they make purchase decisions. These models are linear, sequential models built on an assumption that consumers move through a series
AIDA_(marketing)
Subset of artificial intelligence
other models that were later found to be reinventions of the generalised linear models of statistics. Probabilistic reasoning was also employed, especially
Machine_learning
languages that can be solved by a deterministic Turing machine with the same assumptions about tape length. Because there are only a polynomial number of distinct
NL-complete
Estimate of time taken for running an algorithm
that have linear or greater total work (allowing them to read the entire input), but sub-linear depth. Algorithms that have guaranteed assumptions on the
Time_complexity
Hypothesis in computational complexity theory
In computational complexity theory, a computational hardness assumption is the hypothesis that a particular problem cannot be solved efficiently (where
Computational hardness assumption
Computational_hardness_assumption
Regression analysis
least squares and non-linear least squares. The assumption underlying this procedure is that the model can be approximated by a linear function, namely a
Nonlinear_regression
Machine learning technique
i.e., models that make very few assumptions about the data, which are typically simple decision trees. When a decision tree is the weak learner, the resulting
Gradient_boosting
Regularization technique for ill-posed problems
is particularly useful to mitigate the problem of multicollinearity in linear regression, which commonly occurs in models with large numbers of parameters
Ridge_regression
Specialized form of regression analysis, in statistics
properties if their underlying assumptions are true, but can give misleading results otherwise (i.e. are not robust to assumption violations). Robust regression
Robust_regression
Data organization and storage formats
sorting a list. For a structure that isn't ordered, on the other hand, no assumptions can be made about the ordering of the elements (although a physical implementation
List_of_data_structures
Collection of statistical models
do not rely on an assumption of normality. Below we make clear the connection between multi-way ANOVA and linear regression. Linearly re-order the data
Analysis_of_variance
Statistical hypothesis test
t-test. Notice that the assumption of equal variance, var.equal=T, is required to make the analysis exactly equivalent to simple linear regression. > with(word
Student's_t-test
Infinite sequence of numbers satisfying a linear equation
equation is called a linear recurrence relation. The concept is also known as a linear recurrence sequence, linear-recursive sequence, linear-recurrent sequence
Constant-recursive_sequence
Type of statistical model
part in partially linear model. Source: Wolfgang, Hua Liang and Jiti Gao consider the assumptions and remarks of partially linear model under fixed and
Partially_linear_model
Statistical relationship
data. It usually refers to the extent to which a pair of quantities are linearly related. More generally, an arbitrary relationship between variables is
Correlation
Resource problem in machine learning
adaptively. Generalized linear algorithms: The reward distribution follows a generalized linear model, an extension to linear bandits. KernelUCB algorithm:
Multi-armed_bandit
Statistical model for a binary dependent variable
regression does not require the multivariate normal assumption of discriminant analysis. The assumption of linear predictor effects can easily be relaxed using
Logistic_regression
Class in computational complexity theory
outside NC and so cannot be effectively parallelized, under the unproven assumption that NC ≠ P. If we use the stronger log-space reduction, this remains
P-complete
Impossible task in computing
the DPLL algorithm. For more general decision problems of first-order theories, conjunctive formulas over linear real or rational arithmetic can be decided
Entscheidungsproblem
Method of data analysis
(tacit) assumptions made in its derivation. In particular, PCA can capture linear correlations between the features but fails when this assumption is violated
Principal_component_analysis
Probabilistic classification algorithm
predictors. The highly unrealistic nature of this assumption, called the naive independence assumption, is what gives the classifier its name. These classifiers
Naive_Bayes_classifier
Pareto efficiency implies utilitarian rule
theorem, proves that a social welfare function that satisfies these three assumptions, would be the weighted sum of expected individual utility functions.
Harsanyi's utilitarian theorem
Harsanyi's_utilitarian_theorem
Category of regression analysis
function. Linear regression is a restricted case of nonparametric regression where m ( x ) {\displaystyle m(x)} is assumed to be a linear function of
Nonparametric_regression
Concept in statistics
class of vector generalized linear models (VGLMs) was proposed to enlarge the scope of models catered for by generalized linear models (GLMs). In particular
Vector generalized linear model
Vector_generalized_linear_model
Multi-criteria decision analysis method
normalisation, the non-linear distances between single dimension scores and ratios should produce smoother trade-offs. Decision Radar : A free online TOPSIS
TOPSIS
Complete subgraph added to a random graph
planted clique conjecture; it has been used as a computational hardness assumption. A clique in a graph is a subset of vertices, all of which are adjacent
Planted_clique
Assumptions for inference in machine learning
instead of another pattern (e.g., step-functions in decision trees instead of continuous functions in linear regression models). Learning involves searching
Inductive_bias
Algorithm for supervised learning of binary classifiers
specific class. It is a type of linear classifier, i.e. a classification algorithm that makes its predictions based on a linear predictor function combining
Perceptron
Iterative method for finding a linear decision boundary
iterative method in machine learning for finding a linear decision boundary that separates two linearly separable classes. It was developed by Yu-Chi Ho
Ho–Kashyap_algorithm
Aspect of economics
substitution This assumption assures that indifference curves are smooth and convex to the origin and is implicit in the last assumption. This assumption also set
Consumer_choice
Resource-sensitive logic allowing each assumption to be used at most once
32:297-307. J. Ketonen and G. Bellin, 1989. A decision procedure revisited: notes on Direct Logic. In Linear Logic and its Implementation. Relevant logic
Affine_logic
situations, present decisions (for example, resource allocation) must be made with incomplete knowledge of the future or distributional assumptions on the future
Online_optimization
Algorithmic technique
such as machine learning (AdaBoost, Winnow, Hedge), optimization (solving linear programs), theoretical computer science (devising fast algorithm for LPs
Multiplicative weight update method
Multiplicative_weight_update_method
Paradigm in machine learning
simple decision boundaries. In the case of semi-supervised learning, the smoothness assumption additionally yields a preference for decision boundaries
Weak_supervision
Type of mathematical model
making some assumptions relevant to P {\displaystyle {\mathcal {P}}} . There are two assumptions: that height can be approximated by a linear function of
Statistical_model
Problem-solving method
common heuristics involve the use of visual representations, additional assumptions, forward/backward reasoning and simplification. Dual process theory concerns
Heuristic
function f a ( x ) {\displaystyle f_{a}(x)} . Suppose that the decisional Diffie–Hellman assumption holds for F p {\displaystyle \mathbb {F} _{p}} ; then Naor
Naor–Reingold pseudorandom function
Naor–Reingold_pseudorandom_function
Classical problem in combinatorics
requires. The set cover problem can be formulated as the following integer linear program (ILP). For a more compact representation of the covering constraint
Set_cover_problem
American spy thriller television series
Hollywood Reporter criticized the script as "seems to be constructed with the assumption that most of the audience will only be half-watching while scrolling Facebook
Lioness_(American_TV_series)
Regression model for ordinal dependent variables
categories. The model only applies to data that meet the proportional odds assumption, the meaning of which can be exemplified as follows. Suppose there are
Ordered_logit
Generalization of a Markov decision process
observable Markov decision process (POMDP) is a generalization of a Markov decision process (MDP). A POMDP models an agent decision process in which it
Partially observable Markov decision process
Partially_observable_Markov_decision_process
Statistical technique to aid interpretation of data
Linear trend estimation is a statistical technique used to analyze data patterns. Data patterns, or trends, occur when the information gathered tends to
Linear_trend_estimation
Statistical theorem
to the Probability of Majority Decisions. The assumptions of the theorem are that a group wishes to reach a decision by majority vote. One of the two
Condorcet's_jury_theorem
Statistical model for count data
In statistics, Poisson regression is a generalized linear model form of regression analysis used to model count data and contingency tables. Poisson regression
Poisson_regression
Optimization algorithm
lipschitz smooth, then gradient descent converges linearly with a fixed step size. Looser assumptions lead to either weaker convergence guarantees or require
Gradient_descent
Type of research design
With the rANOVA, standard univariate and multivariate assumptions apply. The univariate assumptions are: Normality—For each level of the within-subjects
Repeated_measures_design
Branch of statistics
techniques that are commonly used in statistics include mathematical analysis, linear algebra, stochastic analysis, differential equations, and measure theory
Mathematical_statistics
Concept in economics and decision theory
individuals and businesses. The non-linearity of the utility function for money has profound implications in decision-making processes: in situations where
Utility
Branch of statistics
"These typically involve fewer assumptions of structure and distributional form but usually contain strong assumptions about independencies". The main
Parametric_statistics
Set of methods for supervised statistical learning
Chervonenkis (1974). In addition to performing linear classification, SVMs can efficiently perform non-linear classification using the kernel trick, representing
Support_vector_machine
Mathematical relation assigning a probability event to a cost
In mathematical optimization and decision theory, a loss function or cost function (sometimes also called an error function) is a function that maps an
Loss_function
Statement that is taken to be true
An axiom, postulate, or assumption, is a statement that is taken to be true, to serve as a premise or starting point for further reasoning and arguments
Axiom
Statistical methods for comparing samples
β {\displaystyle 1-\beta } . To ensure valid results, the following assumptions must be met: Independent random samples: The samples must be drawn independently
Two-proportion_Z-test
Mathematical analysis technique
in g is linear with L, which can be deduced from the fact that the partial with respect to (w.r.t.) L does not depend on L. Thus the linear "approximation"
Experimental uncertainty analysis
Experimental_uncertainty_analysis
Concept in economics
expected utility hypothesis is a foundational assumption in mathematical economics concerning decision making under uncertainty. It postulates that rational
Expected_utility_hypothesis
Theory of behavioral economics
decision weights are closer to unity when probabilities are low than when they are high. In prospect theory, π {\displaystyle \pi } is never linear.
Prospect_theory
Measure of linear correlation
unqualified correlation coefficient, is a correlation coefficient that measures linear correlation between two sets of data. It is the ratio between the covariance
Pearson correlation coefficient
Pearson_correlation_coefficient
Interpretation of probability
axioms, entails the dynamic assumption. Not one entails Bayesianism. So the personalist requires the dynamic assumption to be Bayesian. It is true that
Bayesian_probability
Theory of organizational decision-making
the newer models that have been proposed make assumptions returning to a consequential view of decision making, as well as assuming that individual preferences
Garbage_can_model
Transforming data by taking the logarithm
that the homoscedasticity assumption (in addition to the linearity assumption) holds true on the transformed variables and linear regression may therefore
Log transformation (statistics)
Log_transformation_(statistics)
Method of statistical inference
the i.i.d. assumption is also absurd. Layers of philosophical concerns. The probability of statistical significance is a function of decisions made by
Statistical_hypothesis_test
Framework for modeling optimization problems that involve uncertainty
action. The considered two-stage problem is linear because the objective functions and the constraints are linear. Conceptually this is not essential and
Stochastic_programming
Model of the planning process
substantially. However, there are a lot of assumptions, requirements without which the rational decision model is a failure. Therefore, they all have
Rational_planning_model
Transmission of information
classify communication is to distinguish between linear transmission, interaction, and transaction models. Linear transmission models focus on how a sender transmits
Communication
Concept in decision theory
confident outcomes which could be a generalized form. The assumption about combining linearly the individual utilities and making the resulting number
Lottery_(decision_theory)
Least-weight tree connecting graph vertices
therefore yields a spanning tree with a smaller weight. This contradicts the assumption that B is an MST. More generally, if the edge weights are not all distinct
Minimum_spanning_tree
Mathematical model used for classification or regression
training data-set by the linear classifier method. Using the joint feature vector ϕ ( x , y ) {\displaystyle \phi (x,y)} , the decision function is defined
Discriminative_model
Regression models that combine parametric and nonparametric models
The most popular methods are the partially linear, index and varying coefficient models. A partially linear model is given by Y i = X i ′ β + g ( Z i )
Semiparametric_regression
Number of values in the final calculation of a statistic that are free to vary
the context of linear models (linear regression, analysis of variance), where certain random vectors are constrained to lie in linear subspaces, and the
Degrees of freedom (statistics)
Degrees_of_freedom_(statistics)
Research field that lies at the intersection of machine learning and computer security
under the assumption that the training and test data are generated from the same statistical distribution (IID). However, this assumption is often violated
Adversarial_machine_learning
Narrative technique
the preceding story, thus forcing the reader to question their prior assumptions about the text. This motif is often used within noir fiction and films
Plot_twist
Study of collection and analysis of data
intervals, linear regression, and correlation; (follow-on) courses may include forecasting, time series, decision trees, multiple linear regression,
Statistics
Statistical property
heteroscedasticity. One of the assumptions of the classical linear regression model is that there is no heteroscedasticity. Breaking this assumption means that the Gauss–Markov
Homoscedasticity and heteroscedasticity
Homoscedasticity_and_heteroscedasticity
Method of statistical analysis
Bayesian linear regression is a type of conditional modeling in which the mean of one variable is described by a linear combination of other variables
Bayesian_linear_regression
Signal processing computational method
sources from the observed total signal. When the statistical independence assumption is correct, blind ICA separation of a mixed signal gives very good results
Independent component analysis
Independent_component_analysis
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DECISION LINEAR-ASSUMPTION
DECISION LINEAR-ASSUMPTION
DECISION LINEAR-ASSUMPTION
DECISION LINEAR-ASSUMPTION
DECISION LINEAR-ASSUMPTION
DECISION LINEAR-ASSUMPTION
DECISION LINEAR-ASSUMPTION
DECISION LINEAR-ASSUMPTION
DECISION LINEAR-ASSUMPTION
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