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  2. Fibonacci sequence - Wikipedia

    en.wikipedia.org/wiki/Fibonacci_sequence

    In mathematics, the Fibonacci sequence is a sequence in which each element is the sum of the two elements that precede it. Numbers that are part of the Fibonacci sequence are known as Fibonacci numbers, commonly denoted F n .

  3. Dynamic systems development method - Wikipedia

    en.wikipedia.org/wiki/Dynamic_systems...

    The DSDM Agile Project Framework is an iterative and incremental approach that embraces principles of Agile development, including continuous user/customer involvement. DSDM fixes cost, quality and time at the outset and uses the MoSCoW prioritisation of scope into musts , shoulds , coulds and will not haves to adjust the project deliverable to ...

  4. Best, worst and average case - Wikipedia

    en.wikipedia.org/wiki/Best,_worst_and_average_case

    For example, the best case for a simple linear search on a list occurs when the desired element is the first element of the list. Development and choice of algorithms is rarely based on best-case performance: most academic and commercial enterprises are more interested in improving average-case complexity and worst-case performance. Algorithms ...

  5. Algorithm - Wikipedia

    en.wikipedia.org/wiki/Algorithm

    Flowchart of using successive subtractions to find the greatest common divisor of number r and s. In mathematics and computer science, an algorithm (/ ˈ æ l ɡ ə r ɪ ð əm / ⓘ) is a finite sequence of mathematically rigorous instructions, typically used to solve a class of specific problems or to perform a computation. [1]

  6. Fibonacci retracement - Wikipedia

    en.wikipedia.org/wiki/Fibonacci_retracement

    In finance, Fibonacci retracement is a method of technical analysis for determining support and resistance levels. [1] It is named after the Fibonacci sequence of numbers, [ 1 ] whose ratios provide price levels to which markets tend to retrace a portion of a move, before a trend continues in the original direction.

  7. Expectation–maximization algorithm - Wikipedia

    en.wikipedia.org/wiki/Expectation–maximization...

    These parameter-estimates are then used to determine the distribution of the latent variables in the next E step. It can be used, for example, to estimate a mixture of gaussians, or to solve the multiple linear regression problem. [2] EM clustering of Old Faithful eruption data. The random initial model (which, due to the different scales of ...

  8. Baum–Welch algorithm - Wikipedia

    en.wikipedia.org/wiki/Baum–Welch_algorithm

    An observation sequence is given by = (=, =, …, =). Thus we can describe a hidden Markov chain by θ = ( A , B , π ) {\displaystyle \theta =(A,B,\pi )} . The Baum–Welch algorithm finds a local maximum for θ ∗ = a r g m a x θ ⁡ P ( Y ∣ θ ) {\displaystyle \theta ^{*}=\operatorname {arg\,max} _{\theta }P(Y\mid \theta )} (i.e. the HMM ...

  9. Kernel density estimation - Wikipedia

    en.wikipedia.org/wiki/Kernel_density_estimation

    Kernel density estimation of 100 normally distributed random numbers using different smoothing bandwidths.. In statistics, kernel density estimation (KDE) is the application of kernel smoothing for probability density estimation, i.e., a non-parametric method to estimate the probability density function of a random variable based on kernels as weights.