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Topics referred to by the same term
optimality in Wiktionary, the free dictionary. Optimality may refer to: Mathematical optimization Optimality theory in linguistics Optimality model,
Optimality
predictions about an organism's optimal behavior or other aspects of its phenotype. Optimality modeling is the modeling aspect of optimization theory.
Optimality_model
Experimental design that is optimal with respect to some statistical criterion
constructing approximately optimal designs, depending on the model specified and the optimality criterion. Users may use a standard optimality-criterion or may
Optimal_experimental_design
Mathematical model of animal foraging behavior
The marginal value theorem (MVT) is an optimality model that usually describes the behavior of an optimally foraging individual in a system where resources
Marginal_value_theorem
Linguistic model for phonological analysis
delimiters. Optimality theory (frequently abbreviated OT) is a linguistic model proposing that the observed forms of language arise from the optimal satisfaction
Optimality_theory
Topics referred to by the same term
optimisation, or optimality may also refer to: Engineering optimization Feedback-directed optimisation, in computing Optimality model in biology Optimality theory
Optimization_(disambiguation)
Behavioral ecology model
use to achieve this goal. OFT is an ecological application of the optimality model. This theory assumes that the most economically advantageous foraging
Optimal_foraging_theory
Mating ritual in hermaphroditic flatworms
Bateman's principle, almost always burdens the mother. Thus, from an optimality model it is usually preferable for an organism to inseminate than to be inseminated
Penis_fencing
Necessary condition for optimality associated with dynamic programming
simpler subproblems, as Bellman's "principle of optimality" prescribes. It is a necessary condition for optimality. The "value" of a decision problem at a certain
Bellman_equation
Weakly optimal allocation of resources
nonsatiation to get to a weak Pareto optimum. Constrained Pareto efficiency is a weakening of Pareto optimality, accounting for the fact that a potential
Pareto_efficiency
statistics, an optimality criterion provides a measure of the fit of the data to a given hypothesis, to aid in model selection. A model is designated as
Optimality_criterion
Statistics and machine learning technique
better results than the average of all the individual models. It can also be proved that if the optimal weighting scheme is used, then a weighted averaging
Ensemble_learning
Searching for wild food resources
Behavioral ecologists use economic models and categories to understand foraging; many of these models are a type of optimal model. Thus foraging theory is discussed
Foraging
Type of machine learning model
A large language model (LLM) is an AI model (typically a neural network) trained on a vast amount of text for natural language processing tasks, especially
Large_language_model
Statistical law in machine learning
compute available. Chinchilla optimality was defined as "optimal for training compute", whereas in actual production-quality models, there will be a lot of
Neural_scaling_law
Mathematical way of attaining a desired output from a dynamic system
certain optimality criterion is achieved. A control problem includes a cost functional that is a function of state and control variables. An optimal control
Optimal_control
Mathematical modelling alogorithm
feedforward networks of optimal complexity, adapting to the noise level in the data and minimising overfitting, ensuring that the resulting model is accurate and
Group_method_of_data_handling
Advanced method of process control
Christopher V.; Scokaert, Pierre O. M. (2000). "Constrained model predictive control: stability and optimality". Automatica. 36 (6): 789–814. doi:10.1016/S0005-1098(99)00214-9
Model_predictive_control
Soviet–Ukrainian mathematician and computer scientist
construction of the optimal model. In the early 1980s Ivakhnenko had established an organic analogy between the problem of constructing models for noisy data
Alexey_Ivakhnenko
Diagnostic plot of binary classifier ability
probability on the x-axis. ROC analysis provides tools to select possibly optimal models and to discard suboptimal ones independently from (and prior to specifying)
Receiver operating characteristic
Receiver_operating_characteristic
Process of calculating the causal factors that produced a set of observations
the physical system: it is the solution of the mathematical model's equation. In optimal control theory, these equations are referred to as the state
Inverse_problem
Hypothesis in neuroscience
'generative' model of how that data is caused and then uses these inferences to guide action. Bayes' rule characterizes the probabilistically optimal inversion
Free_energy_principle
Process of finding the optimal set of variables for a machine learning algorithm
Hyperparameter optimization determines the set of hyperparameters that yields an optimal model which minimizes a predefined loss function on a given data set. The
Hyperparameter_optimization
Production scheduling model
policy is still optimal with quantity discounts. Perera et al. (2017) establish this optimality and fully characterize the (s,S) optimality within the EOQ
Economic_order_quantity
Study of interactions between travellers and infrastructure
number of traffic flow models like Gipps's model, Payne's model, Newell's optimal velocity (OV) model, Wiedemann's model, Whitham's model, the Nagel-Schreckenberg
Traffic_flow
Species of bird
in the diving behaviour of the pochard, Aythya ferina: a test of an optimality model". Animal Behaviour. 48 (2): 457–465. Bibcode:1994AnBeh..48..457C. doi:10
Common_pochard
individual expressed discontent. This system was known as the optimum gender of rearing model (OGR model) which attempted to define a binary for intersex children
Definitions_of_intersex
Mathematical model in optimal trade execution
The Almgren–Chriss model is a mathematical model in mathematical finance for the optimal execution of large portfolio transactions. Developed by Robert
Almgren–Chriss_model
Statistical approach
Invariance, admissibility, and optimality". Approximate designs for polynomial regression: Invariance, admissibility, and optimality. Handbook of Statistics
Response_surface_methodology
Computer science concept
(or access probabilities). Optimal BSTs are generally divided into two types: static and dynamic. In the static optimality problem, the tree cannot be
Optimal_binary_search_tree
Machine learning method to transfer knowledge from a large model to a smaller one
distillation or model distillation is the process of transferring knowledge from a large model to a smaller one. While large models (such as very deep
Knowledge_distillation
Measure of prediction accuracy of a forecast
practical point of view and on a theoretical one, since the existence of an optimal model and the consistency of the empirical risk minimization can be proved
Mean absolute percentage error
Mean_absolute_percentage_error
Field of machine learning
"current" [on-policy] or the optimal [off-policy] one). These methods rely on the theory of Markov decision processes, where optimality is defined in a sense
Reinforcement_learning
Portfolio optimization model in finance
In finance, the Markowitz model ─ put forward by Harry Markowitz in 1952 ─ is a portfolio optimization model; it assists in the selection of the most efficient
Markowitz_model
Large language model by Meta AI
Language Model Meta AI" serving as a backronym) is a family of large language models (LLMs) released by Meta AI starting in February 2023. Llama models come
Llama_(language_model)
Study of mathematical algorithms for optimization problems
sufficient to establish at least local optimality. The envelope theorem describes how the value of an optimal solution changes when an underlying parameter
Mathematical_optimization
Language models designed for reasoning tasks
A reasoning model, also known as a reasoning language model (RLM) or large reasoning model (LRM), is a type of large language model (LLM) that has been
Reasoning_model
Parameter controlling the machine learning process
Hyperparameter optimization finds a tuple of hyperparameters that yields an optimal model which minimizes a predefined loss function on given test data. The objective
Hyperparameter (machine learning)
Hyperparameter_(machine_learning)
Engineering model
sequential design, optimal experimental design (OED) or active learning) Construction of the surrogate model and optimizing the model parameters (i.e.,
Surrogate_model
Machine learning algorithm
selection. It aims to determine which features or variables are crucial for optimal model performance when provided with a dataset and a prediction task. abess
Abess
Class of mathematical problems
Black–Scholes model#American options for various valuation methods here, as well as Fugit for a discrete, tree based, calculation of the optimal time to exercise
Optimal_stopping
Technique for the generative modeling of a continuous probability distribution
diffusion models, also known as diffusion-based generative models or score-based generative models, are a class of latent variable generative models. A diffusion
Diffusion_model
Economic-political theory
local governments are able to provide the optimal level of public goods. Tiebout first proposed the model informally as a graduate student in a seminar
Tiebout_model
Measure of value difference between best possible decision and made decision
actual decision made and what would have been the optimal decision in hindsight. Unlike traditional models that consider regret as merely a post-decision
Regret_(decision_theory)
American linguist
Optimality Theory, a grammar formalism providing a formal theory of cross-linguistic typology (or Universal Grammar) within linguistics. Optimality Theory
Paul_Smolensky
Machine learning library created by Google
identifying optimal models for downstream tasks given a large repository of alternatives.[citation needed] TensorFlow Hub hosts a variety of models across
TensorFlow_Hub
Transition of human species to anthropologically modern behavior
particular, Shea cautions that population pressure, cultural change, or optimality models, like those in human behavioral ecology, might better predict changes
Behavioral_modernity
Branch of economics studying next-best alternatives
or more optimality conditions cannot be satisfied. The economists Richard Lipsey and Kelvin Lancaster showed in 1956 that if one optimality condition
Theory_of_the_second_best
Croatian physician and geneticist
its session on June 21, 2007, Parliament adopted the program as the optimal model to solve the problems of poor education structure of Croatian population
Dragan_Primorac
(Alpha) of a few of them; the model finds the optimum portfolio to hold under such conditions. In essence the optimal portfolio consists of two parts:
Treynor–Black_model
Type of large language model
A generative pre-trained transformer (GPT) is a type of large language model (LLM) that is widely used in generative artificial intelligence chatbots
Generative pre-trained transformer
Generative_pre-trained_transformer
Small commercial aircraft which makes short flights on demand
the most optimal model for missions, in which they compare mathematical statistics for a hybrid, turboshaft, and electrical aircraft models. Whereas for
Air_taxi
Economic model
The Baumol–Tobin model is an economic model of the transactions demand for money as developed independently by William Baumol (1952) and James Tobin (1956)
Baumol–Tobin_model
Cognitive heuristic of searching for an acceptable decision
ISSN 0066-4308. PMID 15012463. Byron, Michael (1998). "Satisficing and Optimality". Ethics. 109 (1): 67–93. doi:10.1086/233874. S2CID 170867023. A paper
Satisficing
Three-dimensional representation of a color space
color solid is the three-dimensional representation of a color space or model and can be thought as an analog of, for example, the one-dimensional color
Color_solid
Language model by DeepMind
George van den; Damoc, Bogdan (2022-03-29). "Training Compute-Optimal Large Language Models". arXiv:2203.15556 [cs.CL]. Rae, Jack W.; Borgeaud, Sebastian;
Chinchilla_(language_model)
Method for optimizing information security investments
The Gordon–Loeb model is an economic model that analyzes the optimal level of investment in information security. The benefits of investing in cybersecurity
Gordon–Loeb_model
Statistical measure of the discrepancy between data and an estimation model
an estimation model, such as a linear regression. A small RSS indicates a tight fit of the model to the data. It is used as an optimality criterion in
Residual_sum_of_squares
Non-probabilistic decision-making model
maximin model is a non-probabilistic decision-making model according to which decisions are ranked on the basis of their worst-case outcomes – the optimal decision
Wald's_maximin_model
Technique for improving the efficiency of estimators in conditional moment models
econometrics, optimal instruments are a technique for improving the efficiency of estimators in conditional moment models, a class of semiparametric models that
Optimal_instruments
Integrated tool environment
UPPAAL is an integrated tool environment for modeling, validation and verification of real-time systems modeled as networks of timed automata, extended with
Uppaal_Model_Checker
Experimental design framework
experiment. What will be the optimal experiment design depends on the particular utility criterion chosen. If the model is linear, the prior probability
Bayesian_experimental_design
Social processes through which ideas and actions come to be seen as normal
normalization thus: Normalization consists first of all in positing a model, an optimal model that is constructed in terms of a certain result, and the operation
Normalization_(sociology)
I/O-efficient algorithm regardless of cache size
of the offline optimal replacement strategy To measure the complexity of an algorithm that executes within the cache-oblivious model, we measure the
Cache-oblivious_algorithm
Finance model linking expected return to systematic risk
In finance, the capital asset pricing model (CAPM) is a model used to determine a theoretically appropriate required rate of return of an asset, to make
Capital_asset_pricing_model
Java development environment
Compuware OptimalJ was a model-driven development environment for Java. OptimalJ was first released in 2001 and was then based on Sun Microsystems' open
OptimalJ
Mathematical model to assist inventory levels
single-period or salvageable) model is a mathematical model in operations management and applied economics used to determine optimal inventory levels. It is
Newsvendor_model
Study of strategies for verifying reports
Hellwig develop the one-period version of this problem and confirm the optimality of a debt contract with deterministic verification in the default region
Optimal_auditing
Mathematical model of financial markets
The Black–Scholes /ˌblæk ˈʃoʊlz/ or Black–Scholes–Merton model is a mathematical model for the dynamics of a financial market containing derivative investment
Black–Scholes_model
American philosopher
ISSN 0033-5770. S2CID 83524824. Orzack, Steven Hecht; Sober, Elliott (1994). "Optimality Models and the Test of Adaptationism". The American Naturalist. 143 (3).
Elliott_Sober
Bioeconomic model applied in the fishing industry
costs and revenues. This model can be applied in three primary scenarios: Monopoly; Maximum Sustainable Yield (biological optimum); and Open Access. Profit
Gordon-Schaefer_model
model (LLM) is a type of machine learning model designed for natural language processing tasks such as language generation. LLMs are language models with
List_of_large_language_models
Measure of algorithm performance for large inputs
algorithm is asymptotically optimal. For example, a lower bound theorem might assume a particular abstract machine model, as in the case of comparison
Asymptotically optimal algorithm
Asymptotically_optimal_algorithm
Method to solve optimization problems
variable of the primal is equal to zero. This necessary condition for optimality conveys a fairly simple economic principle. In standard form (when maximizing)
Linear_programming
Algorithms for processing data too large to fit into a computer's main memory at once
external memory model. External memory algorithms are analyzed in an idealized model of computation called the external memory model (or I/O model, or disk access
External_memory_algorithm
Geographical region which efficiently shares a single currency
an optimum currency area, and provides a comparative before-and-after model by which to test the principles of the theory. In theory, an optimal currency
Optimum_currency_area
The Anisotropic Network Model (ANM) is a simple yet powerful tool made for normal mode analysis of proteins, which has been successfully applied for exploring
Anisotropic_Network_Model
Book by James M. Buchanan and Gordon Tullock
Rule, Game Theory, and Pareto Optimality (includes topics such as majority rule and Pareto optimality) 13. Pareto Optimality, External Costs, and Income
The_Calculus_of_Consent
Set of marks along a ruler such that no two pairs of marks are the same distance apart
general term optimal Golomb ruler is used to refer to the second type of optimality. An optimization-based approach to find an optimal Golomb ruler of
Golomb_ruler
Form of psychotherapy
effective counselling. Thus in counselling adolescents the counsellor can optimally model an autonomous life based on the making of realistic decisions, but
Existential_therapy
Large language model developed by Google
PaLM (Pathways Language Model) is a 540 billion-parameter dense decoder-only transformer-based large language model (LLM) developed by Google AI. Researchers
PaLM
Technique in machine learning
Curriculum learning is a technique in machine learning in which a model is trained on examples of increasing difficulty, where the definition of "difficulty"
Curriculum_learning
Advertising Model". Optimal Control Applications and Methods. 4 (2): 179–184. doi:10.1002/oca.4660040207. S2CID 123673289. Sethi, S.P. (2021). Optimal Control
Sethi_model
Analytical framework to study life history strategies used by organisms
relationship dissatisfaction. mathematical modeling quantitative genetics artificial selection demography optimality modeling mechanistic approach Malthusian parameter
Life_history_theory
Algorithm that estimates unknowns from a series of measurements over time
1002/0471221279. ISBN 0-471-41655-X. Three optimality tests with numerical examples are described in Peter, Matisko (2012). "Optimality Tests and Adaptive Kalman Filter"
Kalman_filter
lane width). Models can teach researchers and engineers how to ensure an optimal flow with a minimum number of traffic jams. Traffic models often are the
Traffic_model
Research tradition in linguistics
theory and the minimalist program. Other present-day generative models include optimality theory, categorial grammar, and tree-adjoining grammar. Generative
Generative_grammar
Statistical model for a binary dependent variable
In statistics, a logistic model (or logit model) is a statistical model that models the log-odds of an event as a linear combination of one or more independent
Logistic_regression
Optimization algorithm
the method is equivalent to applying Newton's method to the first-order optimality conditions, or Karush–Kuhn–Tucker conditions, of the problem. Consider
Sequential quadratic programming
Sequential_quadratic_programming
Branch of applied mathematics
part-of-speech tagging can be found as one component of the OpenGrm library. Optimality theory (OT) and maximum entropy (Maxent) phonotactics use algorithmic
Mathematical_linguistics
Branch of applied probability theory
Normative decision theory is concerned with identification of optimal decisions where optimality is often determined by considering an ideal decision maker
Decision_theory
Design and implementation of tax policy maximizing social welfare
literature in this topic. First, it deals with quasi-optimal pricing, looking at four options for Pareto optimality with adjusted commodity prices. Second, they
Optimal_tax
Social psychological theory
deviations from optimality. Individuals will seek out and maintain group memberships that allow this equilibrium to be operated at an optimal level, which
Optimal distinctiveness theory
Optimal_distinctiveness_theory
Problem-solving method
principles are other broad optimality laws [...] Equilibrium notions and homeostatic behavior can also be interpreted as general optimality principles, covering
Heuristic
Greek-American electrical engineer (1942–2026)
games such as chess, Go, and backgammon. “Lessons from AlphaZero for Optimal, Model Predictive, and Adaptive Control" (2022), which introduces a new conceptual
Dimitri_Bertsekas
Theoretical model of sensory neuroscience
coding hypothesis was proposed by Horace Barlow in 1961 as a theoretical model of sensory neuroscience in the brain. Within the brain, neurons communicate
Efficient_coding_hypothesis
Form of causal modeling that fit networks of constructs to data
Structural equation modeling (SEM) is a diverse set of methods used by scientists for both observational and experimental research. SEM is used mostly
Structural_equation_modeling
Mathematical model for sequential decision making under uncertainty
A Markov decision process (MDP) is a mathematical model for sequential decision making when outcomes are uncertain. It is a type of stochastic decision
Markov_decision_process
Mathematical formalism for artificial general intelligence
Pareto optimality is subjective and that any policy can be considered Pareto optimal, which they describe as undermining all previous optimality claims
AIXI
Class of statistical models
linear model (GLM) is a flexible generalization of ordinary linear regression. The GLM generalizes linear regression by allowing the linear model to be
Generalized_linear_model
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