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  2. Log-normal distribution - Wikipedia

    en.wikipedia.org/wiki/Log-normal_distribution

    A log-normal process is the statistical realization of the multiplicative product of many independent random variables, each of which is positive. This is justified by considering the central limit theorem in the log domain (sometimes called Gibrat's law ).

  3. Machine learning in bioinformatics - Wikipedia

    en.wikipedia.org/wiki/Machine_learning_in...

    In addition, machine learning has been applied to systems biology problems such as identifying transcription factor binding sites using Markov chain optimization. [2] Genetic algorithms, machine learning techniques which are based on the natural process of evolution, have been used to model genetic networks and regulatory structures. [2]

  4. Evidence lower bound - Wikipedia

    en.wikipedia.org/wiki/Evidence_lower_bound

    The ELBO is useful because it provides a guarantee on the worst-case for the log-likelihood of some distribution (e.g. ()) which models a set of data. The actual log-likelihood may be higher (indicating an even better fit to the distribution) because the ELBO includes a Kullback-Leibler divergence (KL divergence) term which decreases the ELBO ...

  5. Modelling biological systems - Wikipedia

    en.wikipedia.org/wiki/Modelling_biological_systems

    Modelling biological systems is a significant task of systems biology and mathematical biology. [a] Computational systems biology [b] [1] aims to develop and use efficient algorithms, data structures, visualization and communication tools with the goal of computer modelling of biological systems.

  6. Mathematics of artificial neural networks - Wikipedia

    en.wikipedia.org/wiki/Mathematics_of_artificial...

    Sometimes models are intimately associated with a particular learning rule. A common use of the phrase "ANN model" is really the definition of a class of such functions (where members of the class are obtained by varying parameters, connection weights, or specifics of the architecture such as the number of neurons, number of layers or their ...

  7. Statistical learning theory - Wikipedia

    en.wikipedia.org/wiki/Statistical_learning_theory

    Supervised learning involves learning from a training set of data. Every point in the training is an input–output pair, where the input maps to an output. The learning problem consists of inferring the function that maps between the input and the output, such that the learned function can be used to predict the output from future input.

  8. 3 Artificial Intelligence (AI) Stocks I'm Loading Up On in 2025

    www.aol.com/3-artificial-intelligence-ai-stocks...

    Rather than focusing on quarterly earnings beats or temporary market sentiment, my investment strategy centers on identifying companies that can compound value over many years or even decades. The ...

  9. Logit - Wikipedia

    en.wikipedia.org/wiki/Logit

    If p is a probability, then p/(1 − p) is the corresponding odds; the logit of the probability is the logarithm of the odds, i.e.: ⁡ = ⁡ = ⁡ ⁡ = ⁡ = ⁡ (). The base of the logarithm function used is of little importance in the present article, as long as it is greater than 1, but the natural logarithm with base e is the one most often used.