Farkhondeh Alsadat Sajadi
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Farkhondeh Alsadat Sajadi has a total of 11 co-authors. Here are the top co-authors by number of shared publications:
- Hassan Akell
University of Isfahan
3 shared publications - Iraj Kazemi
Department of Statistics, Faculty of Mathematics & Statistics University of Isfahan Isfahan Iran
3 shared publications Antar Bandyopadhyay
2 shared publications- Neda Esmaeeli
University of Isfahan
2 shared publications - Rahul Roy
Indian Statistical Institute, New Delhi, India
2 shared publications - Antar Bandyopadhyay
Indian Statistical Institute, Delhi and Kolkata
1 shared publication
Latest Publications
- Open Access
- 9 April 2024
- Published by Springer Nature in Research Square
On computing the number of distinct values occurring in generalized Dirichlet process samples
This paper studies the generalized Dirichlet process ( \(\mathcal{GDP}\) ) with its main properties, including moments of random weights and tail moments. We present the truncated \(\mathcal{GDP}\ as a finite mixture distribution and assess the error bounds caused by the truncation. This tactic provides more practicable stick-breaking priors in nonparametric Bayesian settings and facilitates computation. We obtain the joint density of random weights, show that the number of distinct values varies on raising the $\mathcal{GDP}$ samples, and present the impact of the precision parameter on this number. We also show that our results coincide with the Dirichlet process \((\mathcal{DP})\) . MSC Classification: 62E15 , 60C05 , 97K60- 9 April 2024
- Published by Springer Nature in Research Square
Open AccessOn computing the number of distinct values occurring in generalized Dirichlet process samples
This paper studies the generalized Dirichlet process ( \(\mathcal{GDP}\) ) with its main properties, including moments of random weights and tail moments. We present the truncated \(\mathcal{GDP}\ as a finite mixture distribution and assess the error bounds caused by the truncation. This tactic provides more practicable stick-breaking priors in nonparametric Bayesian settings and facilitates computation. We obtain the joint density of random weights, show that the number of distinct values varies on raising the $\mathcal{GDP}$ samples, and present the impact of the precision parameter on this number. We also show that our results coincide with the Dirichlet process \((\mathcal{DP})\) . MSC Classification: 62E15 , 60C05 , 97K60 - Open Access
- 27 March 2024
- Published by Springer Nature in Research Square
The Joint Distribution of Generalized Dirichlet Process Samples with Applications to Nonparametric Bayesian Modeling
The Dirichlet process (DP) mixtures offer a flexible approach to nonparametric Bayesian modeling by specifying a unique joint distribution. The Generalized Dirichlet Process (GDP) expands upon the original DP’s stick-breaking process , influencing posterior distributions significantly and enhancing the ability to model intricate processes. This study’s objective is to determine the joint distribution of a random sample X1,. .. , Xn drawn from the GDP, satisfying X1 ∈ A1,. .. , Xn ∈ An for any measurable sets Ai, where i = 1,. .. , n. The GDP distribution is represented as a countable mixture with Bell number, Bn, components. Key properties such as moments, skewness, and kurtosis are discussed. Our innovative distribution encompasses all possible samples, whether they are distinct or identical variables. Specifically, the results for the Dirich-let process align with the Blackwell-MacQueen joint distribution. We explore the clustering behavior of samples from this mixture, evaluate the posterior distributions, and present two illustrative examples to highlight our findings. MSC Classification: 60E05 , 62E15 , 97K60
- 27 March 2024
- Published by Springer Nature in Research Square
Open AccessThe Joint Distribution of Generalized Dirichlet Process Samples with Applications to Nonparametric Bayesian Modeling
The Dirichlet process (DP) mixtures offer a flexible approach to nonparametric Bayesian modeling by specifying a unique joint distribution. The Generalized Dirichlet Process (GDP) expands upon the original DP’s stick-breaking process , influencing posterior distributions significantly and enhancing the ability to model intricate processes. This study’s objective is to determine the joint distribution of a random sample X1,. .. , Xn drawn from the GDP, satisfying X1 ∈ A1,. .. , Xn ∈ An for any measurable sets Ai, where i = 1,. .. , n. The GDP distribution is represented as a countable mixture with Bell number, Bn, components. Key properties such as moments, skewness, and kurtosis are discussed. Our innovative distribution encompasses all possible samples, whether they are distinct or identical variables. Specifically, the results for the Dirich-let process align with the Blackwell-MacQueen joint distribution. We explore the clustering behavior of samples from this mixture, evaluate the posterior distributions, and present two illustrative examples to highlight our findings. MSC Classification: 60E05 , 62E15 , 97K60
- 27 June 2023
- Published by Springer Nature in Methodology and Computing in Applied Probability
- Vol. 25 (3) , 1-12
Construction of Jointly Distributed Random Samples Drawn from the Beta Two-Parameter Process
- 27 June 2023
- Published by Springer Nature in Methodology and Computing in Applied Probability
- Vol. 25 (3) , 1-12
Construction of Jointly Distributed Random Samples Drawn from the Beta Two-Parameter Process
- 2 March 2023
- Published by Cambridge University Press (CUP) in Journal of Applied Probability
- Vol. 60 (3) , 1096-1111
On the probability of rumour survival among sceptics
We study a sceptical rumour model on the non-negative integer line. The model starts with two spreaders at sites 0, 1 and sceptical ignorants at all other natural numbers. Then each sceptic transmits the rumour, independently, to the individuals within a random distance on its right after s/he receives the rumour from at least two different sources. We say that the process survives if the size of the set of vertices which heard the rumour in this fashion is infinite. We calculate the probability of survival exactly, and obtain some bounds for the tail distribution of the final range of the rumour among sceptics. We also prove that the rumour dies out among non-sceptics and sceptics, under the same condition.
- 2 March 2023
- Published by Cambridge University Press (CUP) in Journal of Applied Probability
- Vol. 60 (3) , 1096-1111
On the probability of rumour survival among sceptics
We study a sceptical rumour model on the non-negative integer line. The model starts with two spreaders at sites 0, 1 and sceptical ignorants at all other natural numbers. Then each sceptic transmits the rumour, independently, to the individuals within a random distance on its right after s/he receives the rumour from at least two different sources. We say that the process survives if the size of the set of vertices which heard the rumour in this fashion is infinite. We calculate the probability of survival exactly, and obtain some bounds for the tail distribution of the final range of the rumour among sceptics. We also prove that the rumour dies out among non-sceptics and sceptics, under the same condition.
- 23 March 2021
- Published by Springer Nature in Iranian Journal of Science and Technology, Transactions A: Science
- Vol. 45 (3) , 1005-1014
Network Robustness Based on Inverse Percolation
- 23 March 2021
- Published by Springer Nature in Iranian Journal of Science and Technology, Transactions A: Science
- Vol. 45 (3) , 1005-1014
Network Robustness Based on Inverse Percolation
- Open Access
- 23 December 2020
- Published by Springer Nature in Ricerche di Matematica
- Vol. 72 (2) , 1-36
A generalized Gompertz growth model with applications and related birth-death processes
In this paper, we propose a flexible growth model that constitutes a suitable generalization of the well-known Gompertz model. We perform an analysis of various features of interest, including a sensitivity analysis of the initial value and the three parameters of the model. We show that the considered model provides a good fit to some real datasets concerning the growth of the number of individuals infected during the COVID-19 outbreak, and software failure data. The goodness of fit is established on the ground of the ISRP metric and the $$d_2$$ -distance. We also analyze two time-inhomogeneous stochastic processes, namely a birth-death process and a birth process, whose means are equal to the proposed growth curve. In the first case we obtain the probability of ultimate extinction, being 0 an absorbing endpoint. We also deal with a threshold crossing problem both for the proposed growth curve and the corresponding birth process. A simulation procedure for the latter process is also exploited.- 23 December 2020
- Published by Springer Nature in Ricerche di Matematica
- Vol. 72 (2) , 1-36
Open AccessA generalized Gompertz growth model with applications and related birth-death processes
In this paper, we propose a flexible growth model that constitutes a suitable generalization of the well-known Gompertz model. We perform an analysis of various features of interest, including a sensitivity analysis of the initial value and the three parameters of the model. We show that the considered model provides a good fit to some real datasets concerning the growth of the number of individuals infected during the COVID-19 outbreak, and software failure data. The goodness of fit is established on the ground of the ISRP metric and the $$d_2$$ -distance. We also analyze two time-inhomogeneous stochastic processes, namely a birth-death process and a birth process, whose means are equal to the proposed growth curve. In the first case we obtain the probability of ultimate extinction, being 0 an absorbing endpoint. We also deal with a threshold crossing problem both for the proposed growth curve and the corresponding birth process. A simulation procedure for the latter process is also exploited. - 1 December 2020
- Published by Springer Nature in Indian Journal of Pure and Applied Mathematics
- Vol. 51 (4) , 1661-1671
Rumour Propagation Among Sceptics: The Markovian Case
- 1 December 2020
- Published by Springer Nature in Indian Journal of Pure and Applied Mathematics
- Vol. 51 (4) , 1661-1671
Rumour Propagation Among Sceptics: The Markovian Case
- 1 June 2020
- Published by CMV Verlag in Journal of the Iranian Statistical Society
- Vol. 19 (1) , 69-83
On Conditional Inactivity Time of Failed Components in an (n-k+1)-out-of-n System with Nonidentical Independent Components
In this paper, we study an (n-k+1)-out-of-n system by adopting their components to be statistically independent though nonidentically distributed. By assuming that at least m components at a fixed time have failed while the system is still working, we obtain the mixture representation of survival function for a quantity called the conditional inactivity time ...
- 1 June 2020
- Published by CMV Verlag in Journal of the Iranian Statistical Society
- Vol. 19 (1) , 69-83
On Conditional Inactivity Time of Failed Components in an (n-k+1)-out-of-n System with Nonidentical Independent Components
In this paper, we study an (n-k+1)-out-of-n system by adopting their components to be statistically independent though nonidentically distributed. By assuming that at least m components at a fixed time have failed while the system is still working, we obtain the mixture representation of survival function for a quantity called the conditional inactivity time ...
- 19 November 2018
- Published by Springer Nature in Journal of Statistical Physics
- Vol. 174 (4) , 935-952
On Rumour Propagation Among Sceptics
- 19 November 2018
- Published by Springer Nature in Journal of Statistical Physics
- Vol. 174 (4) , 935-952
On Rumour Propagation Among Sceptics
- Open Access
On rumour propagation among sceptics
Junior, Machado and Zuluaga (2011) studied a model to understand the spread of a rumour. Their model consists of individuals situated at the integer points of the line $\N$. An individual at the origin $0$ starts a rumour and passes it to all individuals in the interval $[0,R_0]$, where $R_0$ is a non-negative random variable. An individual located at $i$ in this interval receives the rumour and transmits it further among individuals in $[i, i+R_i]$ where $R_0$ and $R_i$ are i.i.d. random variables. The rumour spreads in this manner. An alternate model considers individuals seeking to find the rumour from individuals who have already heard it. For this s/he asks individuals to the left of her/him and lying in an interval of a random size. We study these two models, when the individuals are more sceptical and they transmit or accept the rumour only if they receive it from at least two different sources. In stochastic geometry the equivalent of this rumour process is the study of coverage of the space $\N^d$ by random sets. Our study here extends the study of coverage of space and considers the case when each vertex of $\N^d$ is covered by at least two distinct random sets.Open AccessOn rumour propagation among sceptics
Junior, Machado and Zuluaga (2011) studied a model to understand the spread of a rumour. Their model consists of individuals situated at the integer points of the line $\N$. An individual at the origin $0$ starts a rumour and passes it to all individuals in the interval $[0,R_0]$, where $R_0$ is a non-negative random variable. An individual located at $i$ in this interval receives the rumour and transmits it further among individuals in $[i, i+R_i]$ where $R_0$ and $R_i$ are i.i.d. random variables. The rumour spreads in this manner. An alternate model considers individuals seeking to find the rumour from individuals who have already heard it. For this s/he asks individuals to the left of her/him and lying in an interval of a random size. We study these two models, when the individuals are more sceptical and they transmit or accept the rumour only if they receive it from at least two different sources. In stochastic geometry the equivalent of this rumour process is the study of coverage of the space $\N^d$ by random sets. Our study here extends the study of coverage of space and considers the case when each vertex of $\N^d$ is covered by at least two distinct random sets.