scholarly journals A Markov model for seismic hazard analysis along the Hellenic subduction Zone (Greece)

2016 ◽  
Vol 47 (3) ◽  
pp. 1376
Author(s):  
I. Votsi ◽  
G. Tsaklidis ◽  
N. Limnios ◽  
E. Papadimitriou ◽  
F. Vallianatos
Keyword(s):  
Seismic Hazard ◽  
Markov Model ◽  
Subduction Zone ◽  
Hazard Analysis ◽  
Transition Probability ◽  
Transition Probability Matrix ◽  
Point Of View ◽  
Hitting Times ◽  
Stochastic Representation ◽  
Hellenic Subduction Zone

A homogeneous finite–state discrete–time Markov model is applied for the earthquake occurrence in the Hellenic Subduction Zone (Greece), a region accommodating high seismic activity, being a key structure from a seismotectonic point of view. An attempt is made to provide a stochastic representation of the earthquake process and to assess the seismic hazard through the application of the Markov model. The model is applied on a complete data sample comprising strong () eart  h-quakes that occurred in the study area since 1911 up to present. The continuous magnitude scale is divided into appropriate intervals to specify discrete states of the model. As the stochastic behavior of the model is governed by its transition probability matrix, we firstly estimate its well–known maximum likelihood estimator. The estimation of the transition probability matrix leads to the estimation of important indicators of the Markov chain, including hitting times and failure rate functions. The   mean number of steps for the first occurrence of an anticipated earthquake (belonging to the class with the stronger events, which we are more interested in) is estimated along with its variance. In a next step, we calculate the confidence interval of the   aforementioned estimators.

scholarly journals Using weighted Markov SCGM(1,1)c model to forecast gold/oil ,DJIA/gold and USD/XAU ratios

2017 ◽  
Vol 12 (4) ◽  
Author(s):  
Hamidreza Mostafaei ◽  
Shaghayegh Kordnoori ◽  
Shirin Kordnoori
Keyword(s):  
Time Series ◽  
Markov Model ◽  
Transition Probability ◽  
Single Gene ◽  
Development Trend ◽  
Transition Probability Matrix ◽  
Grey Model ◽  
Autocorrelation Coefficient ◽  
Dynamic Information ◽  
The Development Trend

Grey model can be counted as a potent approximation for extracting system dynamic information with only small amount of data. A weighted Markov model is appropriate for predicting the stochastic fluctuating dynamic by a transition probability matrix and normalizing autocorrelation coefficient as weighted and a single gene system cloud grey SCGM(1,1)c  model. It is applied to regulate the development trend of time series. In this paper we employed a weighted Markov SCGM(1,1)c model for predicting the Gold/Oil ,DJIA/Gold and USD/XAU ratios. By examining the forecasted results, it was concluded that the weighted Markov SCGM(1,1)c model is a reliable and effective modeling method.

scholarly journals On the Mathematics of RNA Velocity I: Theoretical Analysis

2020 ◽  
Author(s):  
Tiejun Li ◽  
Jifan Shi ◽  
Yichong Wu ◽  
Peijie Zhou
Keyword(s):  
Data Analysis ◽  
Transition Probability ◽  
Velocity Model ◽  
Transition Probability Matrix ◽  
Point Of View ◽  
Parameter Inference ◽  
Mathematical Basis ◽  
Whole Process ◽  
Downstream Analysis ◽  
The Continuum

AbstractThe RNA velocity provides a new avenue to study the stemness and lineage of cells in the development in scRNA-seq data analysis. Some promising extensions of it are proposed and the community is experiencing a fast developing period. However, in this stage, it is of prime importance to revisit the whole process of RNA velocity analysis from the mathematical point of view, which will help to understand the rationale and drawbacks of different proposals. The current paper is devoted to this purpose. We present a thorough mathematical study on the RNA velocity model from dynamics to downstream data analysis. We derived the analytical solution of the RNA velocity model from both deterministic and stochastic point of view. We presented the parameter inference framework based on the maximum likelihood estimate. We also derived the continuum limit of different downstream analysis methods, which provides insights on the construction of transition probability matrix, root and endingcells identification, and the development routes finding. The overall analysis aims at providing a mathematical basis for more advanced design and development of RNA velocity type methods in the future.

Forecasting The Stock Market Values Using Hidden Markov Model

2016 ◽  
Vol 4 (1) ◽  
Author(s):  
R. Sasikumar ◽  
A. Sheik Abdullah
Keyword(s):  
Stock Market ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Financial Market ◽  
Hidden Markov ◽  
Transition Probability ◽  
Price Change ◽  
Transition Probability Matrix ◽  
Market Values ◽  
Computer Scientists

The financial market influences personal corporate financial lives and the economic health of a country. Price change of stock market is not a completely random model. The pattern of financial market has been observed by some economists, statisticians and computer scientists. This paper gives a detailed idea about the sequence and state prediction of stock market using Hidden Markov Model and also making inferences regarding stock market trend. The one day difference in close value of stock market value has been used for some period and the corresponding transition probability matrix and emission probability matrix are obtained. Seven optimal hidden states and three sequences are generated using MATLAB and then compared.

PEMETAAN DAERAH RENTAN GEMPA BUMI SEBAGAI DASAR PERENCANAAN TATA RUANG DAN WILAYAH DI PROVINSI SULAWESI BARAT

KURVATEK ◽  
2017 ◽  
Vol 1 (2) ◽  
pp. 41-47
Author(s):  
Marinda noor Eva
Keyword(s):  
Seismic Hazard ◽  
Hazard Analysis ◽  
Probabilistic Seismic Hazard Analysis ◽  
Seismic Hazard Analysis ◽  
Probabilistic Seismic Hazard

Penelitian mengenai daerah rawan gempa bumi ini menggunakan Metode Probabilistic Seismic Hazard Analysis (PSHA) di Provinsi Sulawesi Barat, dengan tujuan untuk memetakan tingkat kerawanan bahaya gempa bumi di Kabupaten Mamasa. Penelitian ini menggunakan data kejadian gempa bumi di Pulau Sulawesi dan sekitarnya dari tahun 1900 – 2015. Hasil pengolahan PSHA menggunakan Software Ez-Frisk 7.52 yang menghasilkan nilai hazard di batuan dasar pada kondisi PGA (T = 0,0 sekon), dengan periode ulang 500 tahun dan 2500 tahun berkisar antara (149,54 – 439,45) gal dan (287,18 – 762,81) gal. Nilai hazard di batuan dasar dengan kondisi spektra T = 0,2 sekon untuk periode ulang 500 tahun dan 2500 tahun adalah (307,04 – 1010,90) gal dan (569,48 – 1849,78) gal. Nilai hazard di batuan dasar dengan kondisi spektra T = 1,0 sekon untuk periode ulang 500 tahun dan 2500 tahun diperoleh nilai (118,01 – 265,75) gal dan (223,74 – 510,92) gal. Berdasarkan analisis PSHA, nilai PGA di Provinsi Sulawesi Barat dominan dipengaruhi oleh sumber gempa sesar.

PSHA software review based on the Monte Carlo method

2020 ◽  
pp. 14-24
Author(s):  
V.A. Mironov ◽  
S.A. Peretokin ◽  
K.V. Simonov
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Seismic Hazard ◽  
Hazard Analysis ◽  
Software Review ◽  
Seismic Hazard Analysis ◽  
Probabilistic Seismic Hazard ◽  
Test Calculation ◽  
Seismic Surveys ◽  
The Monte Carlo Method

The article is a continuation of the software research to perform probabilistic seismic hazard analysis (PSHA) as one of the main stages in engineering seismic surveys. The article provides an overview of modern software for PSHA based on the Monte Carlo method, describes in detail the work of foreign programs OpenQuake Engine and EqHaz. A test calculation of seismic hazard was carried out to compare the functionality of domestic and foreign software.

On calculating extinction probabilities for branching processes in random environments

1969 ◽  
Vol 6 (03) ◽  
pp. 478-492 ◽  
Author(s):  
William E. Wilkinson
Keyword(s):  
Generating Functions ◽  
Infinite Sequence ◽  
Branching Processes ◽  
Transition Probability ◽  
Transition Probability Matrix ◽  
Random Environments ◽  
Real Numbers ◽  
Discrete Time Markov Chain ◽  
Extinction Probabilities ◽  
Image Position

Consider a discrete time Markov chain {Zn } whose state space is the non-negative integers and whose transition probability matrix ║Pij ║ possesses the representation where {Pr }, r = 1,2,…, is a finite or denumerably infinite sequence of non-negative real numbers satisfying , and , is a corresponding sequence of probability generating functions. It is assumed that Z 0 = k, a finite positive integer.

scholarly journals States Transitions Inference of Postpartum Depression Based on Multi-State Markov Model

2021 ◽  
Vol 18 (14) ◽  
pp. 7449
Author(s):  
Juan Xiong ◽  
Qiyu Fang ◽  
Jialing Chen ◽  
Yingxin Li ◽  
Huiyi Li ◽  
...  
Keyword(s):  
Postpartum Depression ◽  
Markov Model ◽  
Normal State ◽  
Sojourn Time ◽  
Transition Probabilities ◽  
Transition Probability ◽  
Public Health Problem ◽  
Mental States ◽  
Hazard Ratio ◽  
Multiple Time

Background: Postpartum depression (PPD) has been recognized as a severe public health problem worldwide due to its high incidence and the detrimental consequences not only for the mother but for the infant and the family. However, the pattern of natural transition trajectories of PPD has rarely been explored. Methods: In this research, a quantitative longitudinal study was conducted to explore the PPD progression process, providing information on the transition probability, hazard ratio, and the mean sojourn time in the three postnatal mental states, namely normal state, mild PPD, and severe PPD. The multi-state Markov model was built based on 912 depression status assessments in 304 Chinese primiparous women over multiple time points of six weeks postpartum, three months postpartum, and six months postpartum. Results: Among the 608 PPD status transitions from one visit to the next visit, 6.2% (38/608) showed deterioration of mental status from the level at the previous visit; while 40.0% (243/608) showed improvement at the next visit. A subject in normal state who does transition then has a probability of 49.8% of worsening to mild PPD, and 50.2% to severe PPD. A subject with mild PPD who does transition has a 20.0% chance of worsening to severe PPD. A subject with severe PPD is more likely to improve to mild PPD than developing to the normal state. On average, the sojourn time in the normal state, mild PPD, and severe PPD was 64.12, 6.29, and 9.37 weeks, respectively. Women in normal state had 6.0%, 8.5%, 8.7%, and 8.8% chances of progress to severe PPD within three months, nine months, one year, and three years, respectively. Increased all kinds of supports were associated with decreased risk of deterioration from normal state to severe PPD (hazard ratio, HR: 0.42–0.65); and increased informational supports, evaluation of support, and maternal age were associated with alleviation from severe PPD to normal state (HR: 1.46–2.27). Conclusions: The PPD state transition probabilities caused more attention and awareness about the regular PPD screening for postnatal women and the timely intervention for women with mild or severe PPD. The preventive actions on PPD should be conducted at the early stages, and three yearly; at least one yearly screening is strongly recommended. Emotional support, material support, informational support, and evaluation of support had significant positive associations with the prevention of PPD progression transitions. The derived transition probabilities and sojourn time can serve as an importance reference for health professionals to make proactive plans and target interventions for PPD.

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