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The following are among the properties of log-concave distributions: If a density is log-concave, so is its cumulative distribution function (CDF). If a multivariate density is log-concave, so is the marginal density over any subset of variables. The sum of two independent log-concave random variables is log-concave. This follows from the fact ...
SGLD can be applied to the optimization of non-convex objective functions, shown here to be a sum of Gaussians. Stochastic gradient Langevin dynamics (SGLD) is an optimization and sampling technique composed of characteristics from Stochastic gradient descent, a Robbins–Monro optimization algorithm, and Langevin dynamics, a mathematical extension of molecular dynamics models.
Reservoir sampling is a family of randomized algorithms for choosing a simple random sample, without replacement, of k items from a population of unknown size n in a single pass over the items. The size of the population n is not known to the algorithm and is typically too large for all n items to fit into main memory .
Rejection sampling is most often used in cases where the form of () makes sampling difficult. A single iteration of the rejection algorithm requires sampling from the proposal distribution, drawing from a uniform distribution, and evaluating the () / (()) expression. Rejection sampling is thus more efficient than some other method whenever M ...
The HyperLogLog algorithm is able to estimate cardinalities of > 10 9 with a typical accuracy (standard error) of 2%, using 1.5 kB of memory. [1] HyperLogLog is an extension of the earlier LogLog algorithm, [2] itself deriving from the 1984 Flajolet–Martin algorithm. [3]
Log-normal distribution; Logrank test; Lomax distribution; Long-range dependency; Long Tail; Long-tail traffic; Longitudinal study; Longstaff–Schwartz model; Lorenz curve; Loss function; Lot quality assurance sampling; Lotka's law; Low birth weight paradox; Lucia de Berk – prob/stats related court case; Lukacs's proportion-sum independence ...
In computational statistics, the Metropolis-adjusted Langevin algorithm (MALA) or Langevin Monte Carlo (LMC) is a Markov chain Monte Carlo (MCMC) method for obtaining random samples – sequences of random observations – from a probability distribution for which direct sampling is difficult.
In computational fluid dynamics QUICK, which stands for Quadratic Upstream Interpolation for Convective Kinematics, is a higher-order differencing scheme that considers a three-point upstream weighted by quadratic interpolation for the cell face values.