Results 21 to 30 of about 1,160,391 (297)
Data-Centric Engineering, 2021 Monte Carlo algorithms simulates some prescribed number of samples, taking some random real time to complete the computations necessary. This work considers the converse: to impose a real-time budget on the computation, which results in the number of ...
Lawrence M. Murray +2 more
doaj +1 more source
Statistics and Computing, 2021 Motivated mainly by applications to partial differential equations with random coefficients, we introduce a new class of Monte Carlo estimators, called Toeplitz Monte Carlo (TMC) estimator for approximating the integral of a multivariate function with respect to the direct product of an identical univariate probability measure.
Josef Dick, Takashi Goda, Hiroya Murata
openaire +3 more sources
Why Monte Carlo Simulations are Inferences and not Experiments [PDF]
, 2012 Monte Carlo Simulations arrive at their results by introducing randomness, sometimes derived from a physical randomizing device. Nonetheless, we argue, they open no new epistemic channels beyond that already employed by traditional simulations: the ...
John D. Norton +3 more
core +2 more sources
Multilevel Monte Carlo for continuous time Markov chains, with applications in biochemical kinetics [PDF]
, 2012 We show how to extend a recently proposed multi-level Monte Carlo approach to the continuous time Markov chain setting, thereby greatly lowering the computational complexity needed to compute expected values of functions of the state of the system to a ...
Anderson, David, Higham, Desmond
core +4 more sources
SMCTC : sequential Monte Carlo in C++ [PDF]
, 2009 Sequential Monte Carlo methods are a very general class of Monte Carlo methods for sampling from sequences of distributions. Simple examples of these algorithms are used very widely in the tracking and signal processing literature.
Johansen, Adam M., Adam M. Johansen
core +1 more source
Geodesic Monte Carlo on Embedded Manifolds [PDF]
, 2013 Markov chain Monte Carlo methods explicitly defined on the manifold of probability distributions have recently been established. These methods are constructed from diffusions across the manifold and the solution of the equations describing geodesic flows
Simon Byrne +5 more
core +1 more source
E-Jurnal Matematika, 2017 Contract options are the most important part of an investment strategy. An option is a contract that entitles the owner or holder to sell an asset on a designated maturity date.
DEWA AYU AGUNG PUTRI RATNASARI +2 more
doaj +1 more source
Automatic differentiable Monte Carlo: Theory and application
Physical Review Research, 2023 Differentiable programming has emerged as a key programming paradigm empowering rapid developments of deep learning while its applications to important computational methods such as Monte Carlo remain largely unexplored.
Shi-Xin Zhang, Zhou-Quan Wan, Hong Yao
doaj +1 more source
Information-Geometric Markov Chain Monte Carlo Methods Using Diffusions [PDF]
, 2014 Recent work incorporating geometric ideas in Markov chain Monte Carlo is reviewed in order to highlight these advances and their possible application in a range of domains beyond statistics. A full exposition of Markov chains and their use in Monte Carlo
Livingstone, Samuel +5 more
core +1 more source
EPJ Web of Conferences, 2013 Bayesian inference often requires integrating some function with respect to a posterior distribution. Monte Carlo methods are sampling algorithms that allow to compute these integrals numerically when they are not analytically tractable.
Bardenet Rémi
doaj +1 more source