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Probability-matching
Probability matching is a decision strategy in which predictions of class membership are proportional to the class base rates. Thus, if in the training set positive examples are observed 60% of the time, and negative examples are observed 40% of the time, then the observer using a ''probability-matching'' strategy will predict (for unlabeled examples) a class label of "positive" on 60% of instances, and a class label of "negative" on 40% of instances. The optimal Bayesian decision strategy (to maximize the number of correct predictions, see ) in such a case is to always predict "positive" (i.e., predict the majority category in the absence of other information), which has 60% chance of winning rather than matching which has 52% of winning (where ''p'' is the probability of positive realization, the result of matching would be p^2+(1-p)^2, here .6 \times .6+ .4 \times .4). The probability-matching strategy is of psychological interest because it is frequently employed by human ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Thompson Sampling
Thompson sampling, named after William R. Thompson, is a heuristic for choosing actions that addresses the exploration-exploitation dilemma in the multi-armed bandit problem. It consists of choosing the action that maximizes the expected reward with respect to a randomly drawn belief. Description Consider a set of contexts \mathcal, a set of actions \mathcal, and rewards in \mathbb. In each round, the player obtains a context x \in \mathcal, plays an action a \in \mathcal and receives a reward r \in \mathbb following a distribution that depends on the context and the issued action. The aim of the player is to play actions such as to maximize the cumulative rewards. The elements of Thompson sampling are as follows: # a likelihood function P(r, \theta,a,x); # a set \Theta of parameters \theta of the distribution of r; # a prior distribution P(\theta) on these parameters; # past observations triplets \mathcal = \; # a posterior distribution P(\theta, \mathcal) \propto P(\mathcal, ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Decision Strategy
Decision theory (or the theory of choice; not to be confused with Rational choice theory, choice theory) is a branch of applied probability theory concerned with the theory of making decisions based on assigning probabilities to various factors and assigning statistical significance, numerical consequences to the outcome. There are three branches of decision theory: # Normative statement, Normative decision theory: Concerned with the identification of optimal decision, optimal decisions, where optimality is often determined by considering an ideal decision-maker who is able to calculate with perfect accuracy and is in some sense fully rationality, rational. # Decision analysis#Decision analysis as a prescriptive approach, Prescriptive decision theory: Concerned with describing observed behaviors through the use of conceptual model, conceptual models, under the assumption that those making the decisions are behaving under some consistent rules. # Positive statement, Descriptive de ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Base Rates
In probability and statistics, the base rate (also known as prior probabilities) is the class of probabilities unconditional on "featural evidence" (likelihoods). For example, if 1% of the population were medical professionals, and remaining 99% were ''not'' medical professionals, then the base rate of medical professionals will be 1%. The method for integrating base rates and featural evidence is given by Bayes' rule. In the sciences, including medicine, the base rate is critical for comparison. In medicine a treatment's effectiveness is clear when the base rate is available. For example if the control group, using no treatment at all, had their own base rate of 1/20 recoveries within 1 day (meaning 1 out of every 20 people recover in 1 day) and a treatment had a 1/100 base rate of recovery within 1 day, we see that the treatment actively decreases the recovery in the first day for the winter cold. Base rate fallacy A number of psychological studies have examined a phenom ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Bayesian Decision Theory
In estimation theory and decision theory, a Bayes estimator or a Bayes action is an estimator or decision rule that minimizes the posterior expected value of a loss function (i.e., the posterior expected loss). Equivalently, it maximizes the posterior expectation of a utility function. An alternative way of formulating an estimator within Bayesian statistics is maximum a posteriori estimation. Definition Suppose an unknown parameter \theta is known to have a prior distribution \pi. Let \widehat = \widehat(x) be an estimator of \theta (based on some measurements ''x''), and let L(\theta,\widehat) be a loss function, such as squared error. The Bayes risk of \widehat is defined as E_\pi(L(\theta, \widehat)), where the expectation is taken over the probability distribution of \theta: this defines the risk function as a function of \widehat. An estimator \widehat is said to be a ''Bayes estimator'' if it minimizes the Bayes risk among all estimators. Equivalently, the estimator whic ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
New York City
New York, often called New York City or NYC, is the List of United States cities by population, most populous city in the United States. With a 2020 population of 8,804,190 distributed over , New York City is also the List of United States cities by population density, most densely populated major city in the United States, and is more than twice as populous as second-place Los Angeles. New York City lies at the southern tip of New York (state), New York State, and constitutes the geographical and demographic center of both the Northeast megalopolis and the New York metropolitan area, the largest metropolitan area in the world by urban area, urban landmass. With over 20.1 million people in its metropolitan statistical area and 23.5 million in its combined statistical area as of 2020, New York is one of the world's most populous Megacity, megacities, and over 58 million people live within of the city. New York City is a global city, global Culture of New ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Statistical Classification
In statistics, classification is the problem of identifying which of a set of categories (sub-populations) an observation (or observations) belongs to. Examples are assigning a given email to the "spam" or "non-spam" class, and assigning a diagnosis to a given patient based on observed characteristics of the patient (sex, blood pressure, presence or absence of certain symptoms, etc.). Often, the individual observations are analyzed into a set of quantifiable properties, known variously as explanatory variables or ''features''. These properties may variously be categorical (e.g. "A", "B", "AB" or "O", for blood type), ordinal (e.g. "large", "medium" or "small"), integer-valued (e.g. the number of occurrences of a particular word in an email) or real-valued (e.g. a measurement of blood pressure). Other classifiers work by comparing observations to previous observations by means of a similarity or distance function. An algorithm that implements classification, especially in a ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Machine Learning
Machine learning (ML) is a field of inquiry devoted to understanding and building methods that 'learn', that is, methods that leverage data to improve performance on some set of tasks. It is seen as a part of artificial intelligence. Machine learning algorithms build a model based on sample data, known as training data, in order to make predictions or decisions without being explicitly programmed to do so. Machine learning algorithms are used in a wide variety of applications, such as in medicine, email filtering, speech recognition, agriculture, and computer vision, where it is difficult or unfeasible to develop conventional algorithms to perform the needed tasks.Hu, J.; Niu, H.; Carrasco, J.; Lennox, B.; Arvin, F.,Voronoi-Based Multi-Robot Autonomous Exploration in Unknown Environments via Deep Reinforcement Learning IEEE Transactions on Vehicular Technology, 2020. A subset of machine learning is closely related to computational statistics, which focuses on making predicti ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Decision-making
In psychology, decision-making (also spelled decision making and decisionmaking) is regarded as the Cognition, cognitive process resulting in the selection of a belief or a course of action among several possible alternative options. It could be either Rationality, rational or irrational. The decision-making process is a reasoning process based on assumptions of value (ethics and social sciences), values, preferences and beliefs of the decision-maker. Every decision-making process produces a final choice, which may or may not prompt action. Research about decision-making is also published under the label problem solving, particularly in European psychological research. Overview Decision-making can be regarded as a Problem solving, problem-solving activity yielding a solution deemed to be optimal, or at least satisfactory. It is therefore a process which can be more or less Rationality, rational or Irrationality, irrational and can be based on explicit knowledge, explicit or tacit ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
