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Parameter discovery for stochastic biological models against temporal behavioral specifications using an SPRT based Metric for simulated annealing

Faraz Hussain, Raj Gautam Dutta, Sumit Kumar Jha, Christopher James Langmead, Susmit Jha
2012 2012 IEEE 2nd International Conference on Computational Advances in Bio and medical Sciences (ICCABS)  
Stochastic models are often used to study the behavior of biochemical systems and biomedical devices.  ...  Our algorithm uses a combination of simulated annealing and sequential hypothesis testing to reduce the number of samples required for parameter discovery of stochastic models.  ...  CONCLUSIONS We have proposed a new sequential probability ratio test (SPRT) based parameter discovery algorithm for complex stochastic models of biochemical and biomedical systems.  ... 
doi:10.1109/iccabs.2012.6182640 dblp:conf/iccabs/HussainDJLJ12 fatcat:fx2r57uverfmpdone6fdbgtxdm

Stochastic Small Signal Stability of a Power System with Uncertainties

Yan Xu, Fushuan Wen, Hongwei Zhao, Minghui Chen, Zeng Yang, Huiyu Shang
2018 Energies  
The power system is modeled as a set of stochastic differential equations (SDEs).  ...  Hence, it has high computation efficiency compared with the well-established Monte Carlo based method.  ...  Test System Proposed Method Monte Carlo Simulation New England test system 0.019 99.92 145-bus test system 0.022 195.47 Table 3 . 3 Computational Time Comparisons.  ... 
doi:10.3390/en11112980 fatcat:fmmfct66r5g4bdmccrjmi4pa2a

Quantitative and Probabilistic Modeling in Pathway Logic

Alessandro Abate, Yu Bai, Nathalie Sznajder, Carolyn Talcott, Ashish Tiwari
2007 2007 IEEE 7th International Symposium on BioInformatics and BioEngineering  
This paper presents a study of possible extensions of Pathway Logic to represent and reason about semiquantitative and probabilistic aspects of biological processes.  ...  The underlying theme is annotation of reaction rules with affinity information that can be used in different simulation strategies.  ...  This is consistent with the hypothesis that the probabilistic approach models the stochastic nature of the system.  ... 
doi:10.1109/bibe.2007.4375669 dblp:conf/bibe/AbateBSTT07 fatcat:pr76uhkswfhp3fk27hykmbvw7q

Research on the Effectiveness of Probabilistic Stochastic Convolution Neural Network Algorithm in Physical Education Teaching Evaluation

Wei Cui, Gengxin Sun
2022 Computational Intelligence and Neuroscience  
Combining the model with the probabilistic stochastic convolution neural network regression method, the position adaptive probabilistic stochastic convolution neural network integral regression method  ...  Aiming at the problem of optimization contradiction existing in the traditional probabilistic stochastic convolution neural network regression method, a position adaptive probabilistic stochastic convolution  ...  Conflicts of Interest e authors declare that they have no conflicts of interest or personal relationships that could have appeared to influence the work reported in this paper.  ... 
doi:10.1155/2022/4921846 pmid:35528362 pmcid:PMC9068316 fatcat:qyzimwx2gjaoldrbagzej2xola

A Probabilistic Assessment Method for Voltage Stability Considering Large Scale Correlated Stochastic Variables

Jing Zhang, Luqin Fan, Ying Zhang, Gang Yao, Peijia Yu, Guojiang Xiong, Ke Meng, Xiangping Chen, Zhaoyang Dong
2019 IEEE Access  
Case studies with two modified IEEE test systems show that the proposed method is accurate and efficient.  ...  when considering the actual operating characteristics of the power system; and (ii) how to obtain the samples characterized with the specified distribution and the desired correlation.  ...  PROBABILISTIC VOLTAGE STABILITY MODEL A. STOCHASTIC SOURCES There are more and more stochastic sources in power systems than before.  ... 
doi:10.1109/access.2019.2963280 fatcat:6fwqn4smvrabjjggyk7gfzbylq

SReach: A Bounded Model Checker for Stochastic Hybrid Systems [article]

Qinsi Wang, Paolo Zuliani, Soonho Kong, Sicun Gao, Edmund M. Clarke
2014 arXiv   pre-print
In this paper we describe a new tool, SReach, which solves probabilistic bounded reachability problems for two classes of stochastic hybrid systems.  ...  We demonstrate our method's feasibility by applying SReach to three representative biological models and to additional benchmarks for nonlinear hybrid systems with multiple probabilistic system parameters  ...  Section 3 explains how SReach solves the probabilistic bounded reachability problem by encoding stochastic dynamics and combining SMT-based BMC with statistical tests.  ... 
arXiv:1404.7206v2 fatcat:w2ksrikju5hibc2qqmuinsekxm

Parameter discovery in stochastic biological models using simulated annealing and statistical model checking

Faraz Hussain, Sumit K. Jha, Susmit Jha, Christopher J. Langmead
2014 International Journal of Bioinformatics Research and Applications  
Stochastic models are increasingly used to study the behaviour of biochemical systems.  ...  We present a new parameter discovery algorithm that uses simulated annealing, sequential hypothesis testing, and statistical model checking to learn the parameters in a stochastic model.  ...  model.  ... 
doi:10.1504/ijbra.2014.062998 pmid:24989866 pmcid:PMC4438994 fatcat:br2druphvjea3bslzoh6ja63ky

A Probabilistic Estimation of PV Capacity in Distribution Networks from Aggregated Net-load Data

Lewis Waswa, Munyaradzi Justice Chihota, Bernard Bekker
2021 IEEE Access  
First, it deals with the input modeling processes (stochastic expansion and aggregation), vital for probabilistic customer load profiles that are accurate models of the original data.  ...  VALIDITY OF INPUT MODELING PROCESSES Two conditions influence the representativity of sample-based probabilistic models: first, the adequacy of selected samples in representing the behavior of the original  ... 
doi:10.1109/access.2021.3119467 fatcat:zivfvep5rnarbkmpzd7fpkh42y

Probabilistic testing for stochastic hybrid systems

A. Agung Julius, George J. Pappas
2008 2008 47th IEEE Conference on Decision and Control  
In this paper we propose a testing based method for safety/ reachability analysis of stochastic hybrid systems.  ...  Testing based methods are characterized by analysis based on the execution traces of the system or the simulation thereof.  ...  The authors would like to thank Insup Lee, Georgios Fainekos, and Madhukar Anand for valuable discussions in testing and verification.  ... 
doi:10.1109/cdc.2008.4739166 dblp:conf/cdc/JuliusP08 fatcat:elagdccdpnebvggtpgtmqu4jae

BioMETA: A multiple specification parameter estimation system for stochastic biochemical models [article]

Arfeen Khalid
2020 arXiv   pre-print
Our method is based on combining a multiple hypothesis testing based statistical model checking technique with simulated annealing search to look for a single set of parameter values so that the given  ...  Computational modeling of such systems helps in investigating and predicting the behaviors of the underlying biochemical processes but at the same time introduces the presence of several unknown parameters  ...  Later this neural network could be used to generate hundreds of simulation traces in parallel on GPUs eliminating the need to actually simulate the model.  ... 
arXiv:2001.03781v1 fatcat:xwcgbaqamfbvvm24izisl5lkmi

Automated parameter estimation for biological models using Bayesian statistical model checking

Faraz Hussain, Christopher J Langmead, Qi Mi, Joyeeta Dutta-Moscato, Yoram Vodovotz, Sumit K Jha
2015 BMC Bioinformatics  
Our algorithm uses Bayesian model checking, sequential hypothesis testing, and stochastic optimization to automatically synthesize parameters of probabilistic biological models.  ...  Probabilistic models have gained widespread acceptance in the systems biology community as a useful way to represent complex biological systems.  ...  Central Florida with a Graduate Research Excellence Fellowship (FH).  ... 
doi:10.1186/1471-2105-16-s17-s8 pmid:26679759 pmcid:PMC4674867 fatcat:uzlhgb3idrcerfp6llayt7bu4i

Monte Carlo Based Statistical Model Checking of Cyber-Physical Systems: A Review

Angela Pappagallo, Annalisa Massini, Enrico Tronci
2020 Information  
time or cost) as well as on the environment features, the kind of system model, the language used to define the requirements to be verified, the statistical inference approach used, and the algorithm implementing  ...  Statistical Model Checking (SMC) is a simulation-based approach that holds the promise to overcome such an obstacle by using statistical methods in order to sample the set of scenarios.  ...  Acknowledgments: We thank Alberto Lluch Lafuente for his very useful remarks on a preliminary version of this paper. Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/info11120588 fatcat:fur5l4427ff4zkbdxyqtfuk2fq

SReach: A Probabilistic Bounded Delta-Reachability Analyzer for Stochastic Hybrid Systems [chapter]

Qinsi Wang, Paolo Zuliani, Soonho Kong, Sicun Gao, Edmund M. Clarke
2015 Lecture Notes in Computer Science  
In this paper, we present a new tool SReach, which solves probabilistic bounded reachability problems for two classes of models of stochastic hybrid systems.  ...  We demonstrate SReach's applicability by discussing three representative biological models and additional benchmarks for nonlinear hybrid systems with multiple probabilistic system parameters.  ...  Introduction Stochastic hybrid systems (SHSs) are dynamical systems exhibiting discrete, continuous, and stochastic dynamics.  ... 
doi:10.1007/978-3-319-23401-4_3 fatcat:aqq6jybctndvhhieror7xgo6ru

Quantitative Aspects of Programming Languages and Systems over the past 2^4 years and beyond

Alessandro Aldini
2020 Electronic Proceedings in Theoretical Computer Science  
Hence, they need to be integrated both at the level of system modeling and within the verification methodologies and tools.  ...  Quantitative aspects of computation are related to the use of both physical and mathematical quantities, including time, performance metrics, probability, and measures for reliability and security.  ...  [143] , stochastic extension of the hybrid process algebra HYPE [32, 33, 68] , stochastic extension of the Software Component Ensemble Language for modeling ensemble based autonomous systems [101]  ... 
doi:10.4204/eptcs.312.1 fatcat:4fjvygbppjbq3k5ury2kvue3nq

Probabilistic assessment of power system mode with a varying degree of wind sources integration

Nariman Rahmanov, Viktor Kurbatskiy, Huseyngulu Guliyev, Nikita Tomin, Zaur Mammadov, N. Voropai, S. Senderov, A. Michalevich, H. Guliev
2017 E3S Web of Conferences  
Randomly changing and intermittent nature of this power leads to the stochasticity of the power grid mode, estimation of parameters of which requires application of probabilistic modeling.  ...  In the paper it is proposed an advanced algorithm of probabilistic load flow based on the development of two-point estimation method, the efficiency of which is confirmed on the basis of computational  ...  modeling of system state with wind farms Probabilistic assessment of system state is caused by stochastic variability of generation in the nodes, containing sources with random-intermittent power output  ... 
doi:10.1051/e3sconf/20172502003 fatcat:xjrbpfqj2fd5hcizdxsaja4bxu
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