11 skills found
Beliavsky / Burkardt Fortran 90 CodesJohn Burkardt's Fortran 90 codes and documentation
sherrillmix / ViporPlot one-dimensional data using quasirandom noise and density estimates
virgesmith / HumanleagueMicrosynthesis using quasirandom sampling and/or IPF
TomCrypto / Quasi RdGenerate quasirandom Rd sequences
bracerino / Atat Sqs GuiInteractive interface for preparing and analyzing files for generation of special quasirandom structures (SQS) using ATAT mcsqs
grantslatton / QuasirandomA quasirandom sequence generation library
Ralith / Roberts SequenceAn implementation of the Roberts quasirandom sequence
dendisuhubdy / Quasirandom SequencesA quasirandom or low discrepancy sequence, such as the Faure, Halton, Hammersley, Niederreiter or Sobol sequences
bfocassio / SpecialQuasirandomStructuresCode for generating alloy / disordered structures through the special quasirandom structure (SQS) algorithm
CodePleaseRun / Pi EstimationEstimating π using Monte Calro Simulation
RazaTheLegend / QuasiMonteCarloQuasi-Monte Carlo methods are permutations on the standard Monte Carlo method, which employs supremely uniform quasirandom numbers rather than Monte Carlo’s pseudorandom numbers. This thesis investigates the application of quasi-Monte Carlo methods on the Heston model. Our main focus in this paper is the Broadie-Kaya scheme, which our main algorithms are based on. The Monte Carlo methods provides statistical error estimates; however, this is lost in the quasi-Monte Carlo, but in return provides faster convergence than a standard Monte Carlo. A recent discovery has shown that the randomized quasi-Monte Carlo can preserve the speed of quasi-Monte Carlo, but also reintroduces the error estimates from Monte Carlo methods. For our investigation, we compare the Euler discretization with Full Truncation, the Broadie-Kaya scheme using pseudorandom sequences, then amp up the speed using quasirandom sequences and finally a randomized quasi-Monte Carlo.
