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**Thompson sampling**,^{ [1] }^{ [2] } 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 in choosing the action that maximizes the expected reward with respect to a randomly drawn belief.

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Consider a set of contexts , a set of actions , and rewards in . In each round, the player obtains a context , plays an action and receives a reward 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 ;
- a set of parameters of the distribution of ;
- a prior distribution on these parameters;
- past observations triplets ;
- a posterior distribution , where is the likelihood function.

Thompson sampling consists in playing the action according to the probability that it maximizes the expected reward, i.e. action is chosen with probability

where is the indicator function.

In practice, the rule is implemented by sampling, in each round, parameters from the posterior , and choosing the action that maximizes , i.e. the expected reward given the sampled parameters, the action and the current context. Conceptually, this means that the player instantiates their beliefs randomly in each round according to the posterior distribution, and then acts optimally according to them. In most practical applications, it is computationally onerous to maintain and sample from a posterior distribution over models. As such, Thompson sampling is often used in conjunction with approximate sampling techniques.^{ [2] }

Thompson sampling was originally described by Thompson in 1933^{ [1] }. It was subsequently rediscovered numerous times independently in the context of multi-armed bandit problems.^{ [3] }^{ [4] }^{ [5] }^{ [6] }^{ [7] }^{ [8] } A first proof of convergence for the bandit case has been shown in 1997.^{ [3] } The first application to Markov decision processes was in 2000.^{ [5] } A related approach (see Bayesian control rule) was published in 2010.^{ [4] } In 2010 it was also shown that Thompson sampling is *instantaneously self-correcting*.^{ [8] } Asymptotic convergence results for contextual bandits were published in 2011.^{ [6] } Nowadays, Thompson Sampling has been widely used in many online learning problems: Thompson sampling has also been applied to A/B testing in website design and online advertising;^{ [9] } Thompson sampling has formed the basis for accelerated learning in decentralized decision making;^{ [10] } a Double Thompson Sampling (D-TS) ^{ [11] } algorithm has been proposed for dueling bandits, a variant of traditional MAB, where feedbacks come in the format of pairwise comparison.

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, 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.

A generalization of Thompson sampling to arbitrary dynamical environments and causal structures, known as **Bayesian control rule**, has been shown to be the optimal solution to the adaptive coding problem with actions and observations.^{ [4] } In this formulation, an agent is conceptualized as a mixture over a set of behaviours. As the agent interacts with its environment, it learns the causal properties and adopts the behaviour that minimizes the relative entropy to the behaviour with the best prediction of the environment's behaviour. If these behaviours have been chosen according to the maximum expected utility principle, then the asymptotic behaviour of the Bayesian control rule matches the asymptotic behaviour of the perfectly rational agent.

The setup is as follows. Let be the actions issued by an agent up to time , and let be the observations gathered by the agent up to time . Then, the agent issues the action with probability:^{ [4] }

where the "hat"-notation denotes the fact that is a causal intervention (see Causality), and not an ordinary observation. If the agent holds beliefs over its behaviors, then the Bayesian control rule becomes

- ,

where is the posterior distribution over the parameter given actions and observations .

In practice, the Bayesian control amounts to sampling, in each time step, a parameter from the posterior distribution , where the posterior distribution is computed using Bayes' rule by only considering the (causal) likelihoods of the observations and ignoring the (causal) likelihoods of the actions , and then by sampling the action from the action distribution .

Thompson sampling and upper-confidence bound algorithms share a fundamental property that underlies many of their theoretical guarantees. Roughly speaking, both algorithms allocate exploratory effort to actions that might be optimal and are in this sense "optimistic." Leveraging this property, one can translate regret bounds established for UCB algorithms to Bayesian regret bounds for Thompson sampling^{ [12] } or unify regret analysis across both these algorithms and many classes of problems.^{ [13] }

In statistics, the **likelihood function** measures the goodness of fit of a statistical model to a sample of data for given values of the unknown parameters. It is formed from the joint probability distribution of the sample, but viewed and used as a function of the parameters only, thus treating the random variables as fixed at the observed values.

**Bayesian inference** is a method of statistical inference in which Bayes' theorem is used to update the probability for a hypothesis as more evidence or information becomes available. Bayesian inference is an important technique in statistics, and especially in mathematical statistics. Bayesian updating is particularly important in the dynamic analysis of a sequence of data. Bayesian inference has found application in a wide range of activities, including science, engineering, philosophy, medicine, sport, and law. In the philosophy of decision theory, Bayesian inference is closely related to subjective probability, often called "Bayesian probability".

**Pattern recognition** is the automated recognition of patterns and regularities in data. It has applications in statistical data analysis, signal processing, image analysis, information retrieval, bioinformatics, data compression, computer graphics and machine learning. Pattern recognition has its origins in statistics and engineering; some modern approaches to pattern recognition include the use of machine learning, due to the increased availability of big data and a new abundance of processing power. However, these activities can be viewed as two facets of the same field of application, and together they have undergone substantial development over the past few decades. A modern definition of pattern recognition is:

The field of pattern recognition is concerned with the automatic discovery of regularities in data through the use of computer algorithms and with the use of these regularities to take actions such as classifying the data into different categories.

In statistics, **maximum likelihood estimation** (**MLE**) is a method of estimating the parameters of a probability distribution by maximizing a likelihood function, so that under the assumed statistical model the observed data is most probable. The point in the parameter space that maximizes the likelihood function is called the maximum likelihood estimate. The logic of maximum likelihood is both intuitive and flexible, and as such the method has become a dominant means of statistical inference.

A **Bayesian network**, **Bayes network**, **belief network**, **decision network**, **Bayes(ian) model** or **probabilistic directed acyclic graphical model** is a probabilistic graphical model that represents a set of variables and their conditional dependencies via a directed acyclic graph (DAG). Bayesian networks are ideal for taking an event that occurred and predicting the likelihood that any one of several possible known causes was the contributing factor. For example, a Bayesian network could represent the probabilistic relationships between diseases and symptoms. Given symptoms, the network can be used to compute the probabilities of the presence of various diseases.

In probability theory and statistics, a **Gaussian process** is a stochastic process, such that every finite collection of those random variables has a multivariate normal distribution, i.e. every finite linear combination of them is normally distributed. The distribution of a Gaussian process is the joint distribution of all those random variables, and as such, it is a distribution over functions with a continuous domain, e.g. time or space.

In Bayesian statistics, the **posterior probability** of a random event or an uncertain proposition is the conditional probability that is assigned after the relevant evidence or background is taken into account. "Posterior", in this context, means after taking into account the relevant evidences related to the particular case being examined. For instance, there is a ("non-posterior") probability of a person finding buried treasure if they dig in a random spot, and a posterior probability of finding buried treasure if they dig in a spot where their metal detector rings.

In mathematical statistics, the **Kullback–Leibler divergence** is a measure of how one probability distribution is different from a second, reference probability distribution. Applications include characterizing the relative (Shannon) entropy in information systems, randomness in continuous time-series, and information gain when comparing statistical models of inference. In contrast to variation of information, it is a distribution-wise *asymmetric* measure and thus does not qualify as a statistical *metric* of spread - it also does not satisfy the triangle inequality. In the simple case, a Kullback–Leibler divergence of 0 indicates that the two distributions in question are identical. In simplified terms, it is a measure of surprise, with diverse applications such as applied statistics, fluid mechanics, neuroscience and machine learning.

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- 1 2 Thompson, William R. "On the likelihood that one unknown probability exceeds another in view of the evidence of two samples".
*Biometrika*, 25(3–4):285–294, 1933. - 1 2 Daniel J. Russo, Benjamin Van Roy, Abbas Kazerouni, Ian Osband and Zheng Wen (2018), "A Tutorial on Thompson Sampling", Foundations and Trends in Machine Learning: Vol. 11: No. 1, pp 1-96. https://web.stanford.edu/~bvr/pubs/TS_Tutorial.pdf
- 1 2 J. Wyatt.
*Exploration and Inference in Learning from Reinforcement*. Ph.D. thesis, Department of Artificial Intelligence, University of Edinburgh. March 1997. - 1 2 3 4 P. A. Ortega and D. A. Braun. "A Minimum Relative Entropy Principle for Learning and Acting",
*Journal of Artificial Intelligence Research*, 38, pages 475–511, 2010. - 1 2 M. J. A. Strens. "A Bayesian Framework for Reinforcement Learning",
*Proceedings of the Seventeenth International Conference on Machine Learning*, Stanford University, California, June 29–July 2, 2000, http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.140.1701 - 1 2 B. C. May, B. C., N. Korda, A. Lee, and D. S. Leslie. "Optimistic Bayesian sampling in contextual-bandit problems". Technical report, Statistics Group, Department of Mathematics, University of Bristol, 2011.
- ↑ Chapelle, Olivier, and Lihong Li. "An empirical evaluation of thompson sampling." Advances in neural information processing systems. 2011. http://papers.nips.cc/paper/4321-an-empirical-evaluation-of-thompson-sampling
- 1 2 O.-C. Granmo. "Solving Two-Armed Bernoulli Bandit Problems Using a Bayesian Learning Automaton",
*International Journal of Intelligent Computing and Cybernetics*, 3 (2), 2010, 207-234. - ↑ Ian Clarke. "Proportionate A/B testing", September 22nd, 2011, http://blog.locut.us/2011/09/22/proportionate-ab-testing/
- ↑ Granmo, O. C.; Glimsdal, S. (2012). "Accelerated Bayesian learning for decentralized two-armed bandit based decision making with applications to the Goore Game".
*Applied Intelligence*.**38**(4): 479–488. doi:10.1007/s10489-012-0346-z. hdl: 11250/137969 . - ↑ Wu, Huasen; Liu, Xin; Srikant, R (2016),
*Double Thompson Sampling for Dueling Bandits*, arXiv: 1604.07101 , Bibcode:2016arXiv160407101W - ↑ Daniel J. Russo and Benjamin Van Roy (2014), "Learning to Optimize Via Posterior Sampling", Mathematics of Operations Research, Vol. 39, No. 4, pp. 1221-1243, 2014. https://pubsonline.informs.org/doi/abs/10.1287/moor.2014.0650
- ↑ Daniel J. Russo and Benjamin Van Roy (2013), "Eluder Dimension and the Sample Complexity of Optimistic Exploration", Advances in Neural Information Processing Systems 26, pp. 2256-2264. http://papers.nips.cc/paper/4909-eluder-dimension-and-the-sample-complexity-of-optimistic-exploration.pdf

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