Throughput and Channel Occupancy Time fairness trade-off for Downlink LAA-Cat4 and WiFi Coexistence Based on Markov Chain (poster)

2018 ◽  
Author(s):  
Sudat Tuladhar ◽  
Lei Cao ◽  
Ramanarayanan Viswanathan
Keyword(s):  
Markov Chain ◽  
Trade Off ◽  
Time Fairness ◽  
Channel Occupancy

scholarly journals Postprocessing of MCMC

2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Leah F. South ◽  
Marina Riabiz ◽  
Onur Teymur ◽  
Chris J. Oates
Keyword(s):  
Markov Chain ◽  
General Purpose ◽  
Annual Review ◽  
Publication Date ◽  
Trade Off ◽  
Control Variate ◽  
Convergence Diagnostics ◽  
Bias Variance ◽  
Review State ◽  
Quantities Of Interest

Markov chain Monte Carlo is the engine of modern Bayesian statistics, being used to approximate the posterior and derived quantities of interest. Despite this, the issue of how the output from a Markov chain is postprocessed and reported is often overlooked. Convergence diagnostics can be used to control bias via burn-in removal, but these do not account for (common) situations where a limited computational budget engenders a bias-variance trade-off. The aim of this article is to review state-of-the-art techniques for postprocessing Markov chain output. Our review covers methods based on discrepancy minimization, which directly address the bias-variance trade-off, as well as general-purpose control variate methods for approximating expected quantities of interest. Expected final online publication date for the Annual Review of Statistics and Its Application, Volume 9 is March 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Setting margins for margin buying in China: balancing the trade-off between liquidity and prudence

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Hui Hong ◽  
Chien-Chiang Lee ◽  
Zhicun Bian
Keyword(s):  
Markov Chain ◽  
Stock Prices ◽  
Markov Chain Model ◽  
Stock Exchanges ◽  
Chain Model ◽  
Generation Process ◽  
Trade Off ◽  
Content Type ◽  
Margin Setting ◽  
Buying In

PurposeThe purpose of this paper is to propose a new dynamic margin setting method for margin buying in China and evaluate the validity of its performance with the current margin system adopted by stock exchanges in extreme episodes.Design/methodology/approachThis paper adopts the dynamic conceptual model of Huang et al. (2012) (which is based on Figlewski (1984)) but incorporates Markov chain to describe the data generation process of stock price changes. By applying the model to margin buying contracts for the period of March 16, 2018, to May 2, 2018 (baseline study) and June 15, 2015, to July 27, 2015 (robustness test), the model’s superiority to the current margin system adopted by stock exchanges is also tested.FindingsThe paper has several important findings. First, the margins derived by this system vary with market conditions, rising (declining) when stock prices go down (up), and are generally lower than the requirements imposed by stock exchanges. Second, this margin system induces lower overall percentage of costs than that adopted by stock exchanges. Third, parameter estimation plays an important role on shaping empirical results.Research limitations/implicationsThe primary limitation of this paper lies in the fact that it does not solve the issue of determining optimal parameters of the Markov chain model. On the implication of findings, policy-makers and regulators on supervising margin buying activities may need a tune-up on the current margin system which features static margin requirements. Dynamic margins that incorporate market factors are virtually useful to balance the trade-off between liquidity and prudence.Originality/valueTo the best of the authors’ knowledge, this study is the first of its kind to develop a dynamic margin setting method for margin buying in China, aiming to balance the trade-off between liquidity and prudence. It not only takes into account the uniqueness of Chinese markets but also allows for time variations in both initial and maintenance margins.

Bayesian Applications in Auditory Research

2019 ◽  
Vol 62 (3) ◽  
pp. 577-586 ◽  
Author(s):  
Garnett P. McMillan ◽  
John B. Cannon
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Bayesian Methods ◽  
Prior Distribution ◽  
Interim Analysis ◽  
Iterative Process ◽  
Bayes Theorem ◽  
Sound Therapy ◽  
The Many ◽  
Auditory Research

Purpose This article presents a basic exploration of Bayesian inference to inform researchers unfamiliar to this type of analysis of the many advantages this readily available approach provides. Method First, we demonstrate the development of Bayes' theorem, the cornerstone of Bayesian statistics, into an iterative process of updating priors. Working with a few assumptions, including normalcy and conjugacy of prior distribution, we express how one would calculate the posterior distribution using the prior distribution and the likelihood of the parameter. Next, we move to an example in auditory research by considering the effect of sound therapy for reducing the perceived loudness of tinnitus. In this case, as well as most real-world settings, we turn to Markov chain simulations because the assumptions allowing for easy calculations no longer hold. Using Markov chain Monte Carlo methods, we can illustrate several analysis solutions given by a straightforward Bayesian approach. Conclusion Bayesian methods are widely applicable and can help scientists overcome analysis problems, including how to include existing information, run interim analysis, achieve consensus through measurement, and, most importantly, interpret results correctly. Supplemental Material https://doi.org/10.23641/asha.7822592

A Flow-Preserving Algorithm for the Time-Cost Trade-Off Problem

1982 ◽  
Vol 14 (2) ◽  
pp. 109-113 ◽  
Author(s):  
Suleyman Tufekci
Keyword(s):  
Time Cost ◽  
Trade Off

Dual Goals for Speed and Accuracy on the Same Performance Task

2012 ◽  
Vol 11 (3) ◽  
pp. 118-126 ◽  
Author(s):  
Olive Emil Wetter ◽  
Jürgen Wegge ◽  
Klaus Jonas ◽  
Klaus-Helmut Schmidt
Keyword(s):  
Memory Scanning ◽  
Performance Task ◽  
Performance Tasks ◽  
Trade Off ◽  
Test Experiment ◽  
Trade Offs ◽  
New Finding ◽  
Sternberg Paradigm ◽  
Speed Accuracy ◽  
Speed And Accuracy

In most work contexts, several performance goals coexist, and conflicts between them and trade-offs can occur. Our paper is the first to contrast a dual goal for speed and accuracy with a single goal for speed on the same task. The Sternberg paradigm (Experiment 1, n = 57) and the d2 test (Experiment 2, n = 19) were used as performance tasks. Speed measures and errors revealed in both experiments that dual as well as single goals increase performance by enhancing memory scanning. However, the single speed goal triggered a speed-accuracy trade-off, favoring speed over accuracy, whereas this was not the case with the dual goal. In difficult trials, dual goals slowed down scanning processes again so that errors could be prevented. This new finding is particularly relevant for security domains, where both aspects have to be managed simultaneously.

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