AN MCMC ALGORITHM FOR PARAMETER ESTIMATION IN SIGNALS WITH HIDDEN INTERMITTENT INSTABILITY NAN CHEN ∗ , DIMITRIOS GIANNAKIS ∗ , RADU HERBEI † , AND ANDREW J. MAJDA ∗ Abstract. Prediction of extreme events is a highly important and challenging problemin science, engineering, finance, an...
Particle MCMCParticle Gibbs samplerPiecewise deterministic processesSequential Monte CarloWe develop particle Gibbs samplers for static-parameter estimation in discretely observed piecewise deterministic process (PDPs). PDPs are stochastic processes that jump randomly at a countable number of stopping times but...
Model parameter estimation represents a challenge in many fields. A large number of algorithms exist to address this need. These run the gamut from straightforward steepest descent methods, to genetic algorithms, simulated annealing, regression, Bayesian methods and others (Yang et al., 2019). In ...
Future studies should aim to refine and expand upon the methodologies applied in this research. First, MCMC algorithm can further be improved. For example, by running MCMC for a greater number of steps and employing more advanced algorithms, we can minimize the risk of becoming trapped in local...
研究者们通过“开放”状态(一个或多个状态的聚合)的观测数据, 直接使用最大似然估计(maximum likelihood estimation, MLE) 来估计潜在Markov链的全部转移速率(生成元矩阵). 因此, 产生了一个理论问题: 为什么能够估计出潜在Markov系统的生成元矩阵?有两种可能性. 第一是因为情形简单, Markov链的生成元矩阵可以通过...
in systems biology models, namely, those exploiting the profile likelihood [36], Bayesian approaches using Markov Chain Monte Carlo (MCMC) [37], core-prediction analysis based on spread-searching optimization algorithms [38], and pseudo-global identifiability analysis using a Bayesian framework [39]...
Parameter estimation using Eq. (35) can be carried out with standard non-linear optimisation algorithms such as quasi–Newton or conjugate gradients. In the original paper of [14], the penalty parameter λs is inferred using AIC. For a given value of λs, Eq. (35) is optimised to ...
Parameter estimation in biological models is a common yet challenging problem. In this work we explore the problem for gene regulatory networks modeled by differential equations with unknown parameters, such as decay rates, reaction rates, Michaelis-Menten constants, and Hill coefficients. We explore ...
This paper introduces a novel parameter estimation method for the probability tables of Bayesian network classifiers (BNCs), using hierarchical Dirichlet p
In addition, a simulation study is provided, and the differences between the estimates obtained by the two algorithms are examined. Thus, it is concluded that MCMC is a better choice than MLE for the parameter estimation of the modified Weibull distribution. 1548-7741/ 展开 ...