Results 61 to 70 of about 221,738 (314)

Markov Chain Monte Carlo

, 2020
As we have seen, in the previous chapter, in a Markov chain we have the following two types of distributions, leading to a joint distribution.
Nicolas Chopin, Omiros Papaspiliopoulos
openaire   +2 more sources

Continuous outcome estimation in N‐of‐1 trials for accelerated decision‐making

Epilepsia, EarlyView.
Abstract Objective N‐of‐1 trials aim to determine the therapeutic effect for a single individual. This individualized approach necessitates collecting multiple data points over time through repeated alternating periods of active treatment and a comparator or control condition.
Victoria Defelippe, František Bartoš, Eric‐Jan Wagenmakers, Kees P. J. Braun, Floor E. Jansen, Willem M. Otte   +5 more
wiley   +1 more source

Sample caching Markov chain Monte Carlo approach to boson sampling simulation

New Journal of Physics, 2020
Boson sampling is a promising candidate for quantum supremacy. It requires to sample from a complicated distribution, and is trusted to be intractable on classical computers.
Yong Liu, Min Xiong, Chunqing Wu, Dongyang Wang, Yingwen Liu, Jiangfang Ding, Anqi Huang, Xiang Fu, Xiaogang Qiang, Ping Xu, Mingtang Deng, Xuejun Yang, Junjie Wu   +12 more
doaj   +1 more source

Cruising The Simplex: Hamiltonian Monte Carlo and the Dirichlet Distribution

, 2012
Due to its constrained support, the Dirichlet distribution is uniquely suited to many applications. The constraints that make it powerful, however, can also hinder practical implementations, particularly those utilizing Markov Chain Monte Carlo (MCMC ...
Betancourt, M. J.
core   +1 more source

Long‐term safety of early discontinuation of antiseizure medication after resolution of acute provoked neonatal seizures

Epilepsia, EarlyView.
Abstract Objective To assess long‐term safety of antiseizure medication (ASM) discontinuation after resolution of acute provoked neonatal seizures and prior to hospital discharge. Methods Prospective, observational, comparative effectiveness cohort study of neonates with acute provoked seizures born from July 2015 to March 2018, and followed until ...
Hannah C. Glass, Adam L. Numis, Janet S. Soul, Courtney J. Wusthoff, Monica E. Lemmon, Catherine J. Chu, Shavonne L. Massey, Cameron Thomas, Tayyba Anwar, Madison M. Berl, Jennifer G. Larson, Julie Sturza, Dana Annis, Elizabeth E. Rogers, Linda S. Franck, Charles E. McCulloch, Renée A. Shellhaas   +16 more
wiley   +1 more source

Optimizing Electric Vehicle Charging Scheduling With Deep Q Networks and Long Short‐Term Memory‐Based Electricity and Battery State of Charge Prediction

Energy Science &Engineering, EarlyView.
Schematic diagram showing the proposed approach for EV charging/discharging. ABSTRACT The number of electric vehicles (EVs) on the road is rising as a result of recent advancements in EV technology, and EVs are important to the smart grid economy. Demand response schemes involving electric vehicles have the potential to dramatically reduce the cost of ...
F. Zonuntluanga, Subhasish Deb, Kshetrimayum Robert Singh, Subir Datta, Ahmet Onen, Taha Selim Ustun, Jaesung Jung   +6 more
wiley   +1 more source

Markov Chain Monte Carlo Technology [PDF]

In the past fifteen years computational statistics has been enriched by a powerful, somewhat abstract method of generating variates from a target probability distribution that is based on Markov chains whose stationary distribution is the probability ...
Chib, Siddhartha
core  

A Rapid Post‐Disaster Restoration Method for Networked Microgrids Based on Adaptive Quantum Particle Swarm Optimization

Energy Science &Engineering, EarlyView.
To enhance the power restoration speed of networked microgrids (NMGs) after extreme natural disasters and reduce the power outage of the system, this paper proposes a rapid post‐disaster restoration method for NMGs based co‐optimization of fault repair and load restoration.
Yunfan Zhang, Qiang Li, Jun Zhang, Chaohuang Xu   +3 more
wiley   +1 more source

Schedules of lectures and Monte Carlo method

Lietuvos Matematikos Rinkinys, 2011
In this paper the problem of the construction of schedule of lectures is considered. The Markov chain Monte Carlo method is used. A particular program based on simulated annealing algorithm was created.
Nikolaj Grigorjev, Gediminas Stepanauskas   +1 more
doaj   +1 more source

Reversible jump Markov chain Monte Carlo

, 2010
To appear to MCMC handbook, S. P. Brooks, A. Gelman, G. Jones and X.-L.
Fan, Y, Sisson, S A
core