未经治疗的人胶质母细胞瘤的一种新的随机Gompertz扩散模型分析

IF 0.8 4区 数学 Q3 STATISTICS & PROBABILITY Stochastics and Dynamics Pub Date : 2022-03-21 DOI:10.1142/s0219493722500198
Tuan A. Phan, Shuxun Wang, J. Tian
{"title":"未经治疗的人胶质母细胞瘤的一种新的随机Gompertz扩散模型分析","authors":"Tuan A. Phan, Shuxun Wang, J. Tian","doi":"10.1142/s0219493722500198","DOIUrl":null,"url":null,"abstract":"In this paper, we analyze a new Ito stochastic differential equation model for untreated human glioblastomas. The model was the best fit of the average growth and variance of 94 pairs of a data set. We show the existence and uniqueness of solutions in the positive spatial domain. When the model is restricted in the finite domain [Formula: see text], we show that the boundary point 0 is unattainable while the point [Formula: see text] is reflecting attainable. We prove there is a unique ergodic stationary distribution for any non-zero noise intensity, and obtain the explicit probability density function for the stationary distribution. By using Brownian bridge, we give a representation of the probability density function of the first passage time when the diffusion process defined by a solution passes the point [Formula: see text] firstly. We carry out numerical studies to illustrate our analysis. Our mathematical and numerical analysis confirms the soundness of our randomization of the deterministic model in that the stochastic model will set down to the deterministic model when the noise intensity approaches zero. We also give physical interpretation of our stochastic model and analysis.","PeriodicalId":51170,"journal":{"name":"Stochastics and Dynamics","volume":" ","pages":""},"PeriodicalIF":0.8000,"publicationDate":"2022-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Analysis of a new stochastic Gompertz diffusion model for untreated human glioblastomas\",\"authors\":\"Tuan A. Phan, Shuxun Wang, J. Tian\",\"doi\":\"10.1142/s0219493722500198\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, we analyze a new Ito stochastic differential equation model for untreated human glioblastomas. The model was the best fit of the average growth and variance of 94 pairs of a data set. We show the existence and uniqueness of solutions in the positive spatial domain. When the model is restricted in the finite domain [Formula: see text], we show that the boundary point 0 is unattainable while the point [Formula: see text] is reflecting attainable. We prove there is a unique ergodic stationary distribution for any non-zero noise intensity, and obtain the explicit probability density function for the stationary distribution. By using Brownian bridge, we give a representation of the probability density function of the first passage time when the diffusion process defined by a solution passes the point [Formula: see text] firstly. We carry out numerical studies to illustrate our analysis. Our mathematical and numerical analysis confirms the soundness of our randomization of the deterministic model in that the stochastic model will set down to the deterministic model when the noise intensity approaches zero. We also give physical interpretation of our stochastic model and analysis.\",\"PeriodicalId\":51170,\"journal\":{\"name\":\"Stochastics and Dynamics\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":0.8000,\"publicationDate\":\"2022-03-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Stochastics and Dynamics\",\"FirstCategoryId\":\"100\",\"ListUrlMain\":\"https://doi.org/10.1142/s0219493722500198\",\"RegionNum\":4,\"RegionCategory\":\"数学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"STATISTICS & PROBABILITY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Stochastics and Dynamics","FirstCategoryId":"100","ListUrlMain":"https://doi.org/10.1142/s0219493722500198","RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"STATISTICS & PROBABILITY","Score":null,"Total":0}
引用次数: 2

摘要

本文分析了未经治疗的人胶质母细胞瘤的一种新的Ito随机微分方程模型。该模型是94对数据集的平均增长率和方差的最佳拟合。我们证明了解在正空间域中的存在唯一性。当模型被限制在有限域中[公式:见文]时,我们表明边界点0是不可达的,而点[公式:见文]是反映可达的。证明了对任意非零噪声强度存在唯一的遍历平稳分布,并得到了平稳分布的显式概率密度函数。利用布朗桥给出了由解定义的扩散过程首先通过点[公式:见文]时第一次通过时间的概率密度函数。我们进行数值研究来说明我们的分析。我们的数学和数值分析证实了确定性模型随机化的合理性,即当噪声强度接近于零时,随机模型将降至确定性模型。我们还给出了随机模型和分析的物理解释。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Analysis of a new stochastic Gompertz diffusion model for untreated human glioblastomas
In this paper, we analyze a new Ito stochastic differential equation model for untreated human glioblastomas. The model was the best fit of the average growth and variance of 94 pairs of a data set. We show the existence and uniqueness of solutions in the positive spatial domain. When the model is restricted in the finite domain [Formula: see text], we show that the boundary point 0 is unattainable while the point [Formula: see text] is reflecting attainable. We prove there is a unique ergodic stationary distribution for any non-zero noise intensity, and obtain the explicit probability density function for the stationary distribution. By using Brownian bridge, we give a representation of the probability density function of the first passage time when the diffusion process defined by a solution passes the point [Formula: see text] firstly. We carry out numerical studies to illustrate our analysis. Our mathematical and numerical analysis confirms the soundness of our randomization of the deterministic model in that the stochastic model will set down to the deterministic model when the noise intensity approaches zero. We also give physical interpretation of our stochastic model and analysis.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Stochastics and Dynamics
Stochastics and Dynamics 数学-统计学与概率论
CiteScore
1.70
自引率
0.00%
发文量
49
审稿时长
>12 weeks
期刊介绍: This interdisciplinary journal is devoted to publishing high quality papers in modeling, analyzing, quantifying and predicting stochastic phenomena in science and engineering from a dynamical system''s point of view. Papers can be about theory, experiments, algorithms, numerical simulation and applications. Papers studying the dynamics of stochastic phenomena by means of random or stochastic ordinary, partial or functional differential equations or random mappings are particularly welcome, and so are studies of stochasticity in deterministic systems.
期刊最新文献
Reflected stochastic differential equations driven by standard and fractional Brownian motion Dynamical behaviors of an impulsive stochastic neural field lattice model Reflected BSDEs driven by G-Brownian motion with time-varying Lipschitz coefficients Estimates of constants in the limit theorems for chaotic dynamical systems Binary robustness of random attractors for 2D-Ginzburg–Landau equations with Wong–Zakai noise
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1