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  "body": "\n## Summary\n\nA latent variable model is a statistical model that links observed variables to variables that are never observed.\nThe hidden variables explain the dependence between the observed ones.\nTypical examples are [[factor analysis|4QQQP1Q886]], [[mixture models|C98DKQSTNC]] and hidden Markov models.\nThe likelihood of the observed data is obtained by integrating out the hidden variables, often with the [[expectation–maximization algorithm|11N1AXSXYK]].\n\n## Explanations\n\n### L1 human\n\nIntelligence, wealth or the type of a customer cannot be measured directly, but they leave traces in things that can be measured.\nA latent variable model treats such a quantity as a hidden variable and describes how it shapes the measured data.\nFor example, the answers of a student to many test questions are linked because they all depend on one hidden ability.\nOnce the ability is known, the answers are often modelled as independent of each other.\n",
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