Antar Bandyopadhyay
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Previous Organizations / Institutes
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Antar Bandyopadhyay has a total of 21 co-authors. Here are the top co-authors by number of shared publications:
- Amites Dasgupta
Indian Statistical Institute
4 shared publications - Farkhondeh Sajadi
Indian Statistical Institute, Delhi
4 shared publications - Siva Athreya
International Centre for Theoretical Sciences, TIFR, Bengaluru, India
4 shared publications - Debleena Thacker
Theoretical Statistics and Mathematics Unit Indian Statistical Institute 7 S. J. S. Sansanwal Marg New 110016 Delhi INDIA
3 shared publications - Jeffrey E. Steif
Chalmers University of Technology
2 shared publications - Neeraja Sahasrabudhe
Indian Institute of Science Education and Research Mohali (IISERM), India
2 shared publications
Latest Publications
- Open Access
Generalised De-Preferential Random Graphs
We consider some further generalizations of the novel random graph models as introduced by Bandyopadhyay and Sen \cite{BaSe2025} and find asymptotic for the degree of a fixed vertex and along with the asymptotic degree distribution. We show that in the \emph{case of the inverse power law} the order of these statistics is much slower than the case of the simple inverse function, which was considered in \cite{BaSe2025}. However, the results for the linear case remain exactly the same even after introducing a "shift" parameter.
Open AccessGeneralised De-Preferential Random Graphs
We consider some further generalizations of the novel random graph models as introduced by Bandyopadhyay and Sen \cite{BaSe2025} and find asymptotic for the degree of a fixed vertex and along with the asymptotic degree distribution. We show that in the \emph{case of the inverse power law} the order of these statistics is much slower than the case of the simple inverse function, which was considered in \cite{BaSe2025}. However, the results for the linear case remain exactly the same even after introducing a "shift" parameter.
- Open Access
De-Preferential Attachment Random Graphs
In this work we consider a growing random graph sequence where a new vertex is less likely to join to an existing vertex with high degree and more likely to join to a vertex with low degree. In contrast to the well studied \emph{preferential attachment random graphs} \cite{BarAlb99}, we call such a sequence a \emph{de-preferential attachment random graph model}. We consider two types of models, namely, \emph{inverse de-preferential}, where the attachment probabilities are inversely proportional to the degree and \emph{linear de-preferential}, where the attachment probabilities are proportional to $c-$degree, where $c > 0$ is a constant. For the case when each new vertex comes with exactly one half-edge we show that the degree of a fixed vertex is asymptotically of the order $\sqrt{\log n}$ for the inverse de-preferential case and of the order $\log n$ for the linear case. These show that compared to preferential attachment, the degree of a fixed vertex grows to infinity at a much slower rate for these models. We also show that in both cases limiting degree distributions have exponential tails. In fact we show that for the inverse de-preferential model the tail of the limiting degree distribution is faster than exponential while that for the linear de-preferential model is exactly the $\mbox{Geometric}\left(\frac{1}{2}\right)$ distribution. For the case when each new vertex comes with $m > 1$ half-edges, we show that similar asymptotic results hold for fixed vertex degree in both inverse and linear de-preferential models. Our proofs make use of the martingale approach as well as embedding to certain continuous time age dependent branching processes.Open AccessDe-Preferential Attachment Random Graphs
In this work we consider a growing random graph sequence where a new vertex is less likely to join to an existing vertex with high degree and more likely to join to a vertex with low degree. In contrast to the well studied \emph{preferential attachment random graphs} \cite{BarAlb99}, we call such a sequence a \emph{de-preferential attachment random graph model}. We consider two types of models, namely, \emph{inverse de-preferential}, where the attachment probabilities are inversely proportional to the degree and \emph{linear de-preferential}, where the attachment probabilities are proportional to $c-$degree, where $c > 0$ is a constant. For the case when each new vertex comes with exactly one half-edge we show that the degree of a fixed vertex is asymptotically of the order $\sqrt{\log n}$ for the inverse de-preferential case and of the order $\log n$ for the linear case. These show that compared to preferential attachment, the degree of a fixed vertex grows to infinity at a much slower rate for these models. We also show that in both cases limiting degree distributions have exponential tails. In fact we show that for the inverse de-preferential model the tail of the limiting degree distribution is faster than exponential while that for the linear de-preferential model is exactly the $\mbox{Geometric}\left(\frac{1}{2}\right)$ distribution. For the case when each new vertex comes with $m > 1$ half-edges, we show that similar asymptotic results hold for fixed vertex degree in both inverse and linear de-preferential models. Our proofs make use of the martingale approach as well as embedding to certain continuous time age dependent branching processes. - 6 February 2025
- Published by Springer Nature in Journal of Theoretical Probability
- Vol. 38 (2) , 1-33
Right-Most Position of a Last Progeny Modified Branching Random Walk
- 6 February 2025
- Published by Springer Nature in Journal of Theoretical Probability
- Vol. 38 (2) , 1-33
Right-Most Position of a Last Progeny Modified Branching Random Walk
- 27 December 2021
- Published by Elsevier in Stochastic Processes and their Applications
- Vol. 146, 80-97
SLLN and annealed CLT for random walks in I.I.D. random environment on Cayley trees
- 27 December 2021
- Published by Elsevier in Stochastic Processes and their Applications
- Vol. 146, 80-97
SLLN and annealed CLT for random walks in I.I.D. random environment on Cayley trees
- 1 September 2020
- Published by Cambridge University Press (CUP) in Journal of Applied Probability
- Vol. 57 (3) , 853-865
Strong convergence of infinite color balanced urns under uniform ergodicity
We consider the generalization of the Polya urn scheme with possibly infinitely many colors, as introduced in [37], [4], [5], and [6]. For countably many colors, we prove almost sure convergence of the urn configuration under theuniform ergodicityassumption on the associated Markov chain. The proof uses a stochastic coupling of the sequence of chosen colors with abranching Markov chainon a weightedrandom recursive treeas described in [6], [31], and [26]. Using this coupling we estimate the covariance between any two selected colors. In particular, we re-prove the limit theorem for the classical urn models with finitely many colors.
- 1 September 2020
- Published by Cambridge University Press (CUP) in Journal of Applied Probability
- Vol. 57 (3) , 853-865
Strong convergence of infinite color balanced urns under uniform ergodicity
We consider the generalization of the Polya urn scheme with possibly infinitely many colors, as introduced in [37], [4], [5], and [6]. For countably many colors, we prove almost sure convergence of the urn configuration under theuniform ergodicityassumption on the associated Markov chain. The proof uses a stochastic coupling of the sequence of chosen colors with abranching Markov chainon a weightedrandom recursive treeas described in [6], [31], and [26]. Using this coupling we estimate the covariance between any two selected colors. In particular, we re-prove the limit theorem for the classical urn models with finitely many colors.
- Open Access
S.L.L.N. and C.L.T. for Random Walks in I.I.D. Random Environment on Cayley Trees
We consider the random walk in an independent and identically distributed (i.i.d.) random environment on a Cayley graph of a finite free product of copies of $\mathbb{Z}$ and $\mathbb{Z}_2$. Such a Cayley graph is readily seen to be a regular tree. Under a uniform elipticity assumption on the i.i.d. environment we show that the walk has positive speed and establish the annealed central limit theorem for the graph distance of the walker from the starting point.Open AccessS.L.L.N. and C.L.T. for Random Walks in I.I.D. Random Environment on Cayley Trees
We consider the random walk in an independent and identically distributed (i.i.d.) random environment on a Cayley graph of a finite free product of copies of $\mathbb{Z}$ and $\mathbb{Z}_2$. Such a Cayley graph is readily seen to be a regular tree. Under a uniform elipticity assumption on the i.i.d. environment we show that the walk has positive speed and establish the annealed central limit theorem for the graph distance of the walker from the starting point. - Open Access
Generalized Pólya Urn Schemes with Negative but Linear Reinforcements
In this paper, we consider a new type of urn scheme, where the selection probabilities are proportional to a weight function, which is linear but decreasing in the proportion of existing colours. We refer to it as the \emph{negatively reinforced} urn scheme. We establish almost sure limit of the random configuration for any \emph{balanced} replacement matrix $R$. In particular, we show that the limiting configuration is uniform on the set of colours, if and only if, $R$ is a \emph{doubly stochastic} matrix. We further establish almost sure limit of the vector of colour counts and prove central limit theorems for the random configuration, as well as, for the colour counts.Open AccessGeneralized Pólya Urn Schemes with Negative but Linear Reinforcements
In this paper, we consider a new type of urn scheme, where the selection probabilities are proportional to a weight function, which is linear but decreasing in the proportion of existing colours. We refer to it as the \emph{negatively reinforced} urn scheme. We establish almost sure limit of the random configuration for any \emph{balanced} replacement matrix $R$. In particular, we show that the limiting configuration is uniform on the set of colours, if and only if, $R$ is a \emph{doubly stochastic} matrix. We further establish almost sure limit of the vector of colour counts and prove central limit theorems for the random configuration, as well as, for the colour counts. - Open Access
A New Approach to Pólya Urn Schemes and Its Infinite Color Generalization
In this work we generalize Polya urn schemes with possibly infinitely many colors and extend the earlier models described in [4, 5, 7]. We provide a novel and unique approach of representing the observed sequence of colors in terms a branching Markov chain on random recursion tree. This enables us to derive fairly general asymptotic for our urn schemes. We then illustrate through several examples that our method can easily derive the classical results for finite urns, as well as, many new results for infinite color urns.
Open AccessA New Approach to Pólya Urn Schemes and Its Infinite Color Generalization
In this work we generalize Polya urn schemes with possibly infinitely many colors and extend the earlier models described in [4, 5, 7]. We provide a novel and unique approach of representing the observed sequence of colors in terms a branching Markov chain on random recursion tree. This enables us to derive fairly general asymptotic for our urn schemes. We then illustrate through several examples that our method can easily derive the classical results for finite urns, as well as, many new results for infinite color urns.
- 1 September 2014
- Published by Elsevier in Statistics & Probability Letters
- Vol. 92, 39-44
Random walks in I.I.D. random environment on Cayley trees
- 1 September 2014
- Published by Elsevier in Statistics & Probability Letters
- Vol. 92, 39-44
Random walks in I.I.D. random environment on Cayley trees
- Open Access
Pólya Urn Schemes with Infinitely Many Colors
In this work we introduce a new type of urn model with infinite but countable many colors indexed by an appropriate infinite set. We mainly consider the indexing set of colors to be the $d$-dimensional integer lattice and consider balanced replacement schemes associated with bounded increment random walks on it. We prove central and local limit theorems for the random color of the $n$-th selected ball and show that irrespective of the null recurrent or transient behavior of the underlying random walks, the asymptotic distribution is Gaussian after appropriate centering and scaling. We show that the order of any non-zero centering is always ${\mathcal O}\left(\log n\right)$ and the scaling is ${\mathcal O}\left(\sqrt{\log n}\right)$. The work also provides similar results for urn models with infinitely many colors indexed by more general lattices in ${\mathbb R}^d$. We introduce a novel technique of representing the random color of the $n$-th selected ball as a suitably sampled point on the path of the underlying random walk. This helps us to derive the central and local limit theorems.Open AccessPólya Urn Schemes with Infinitely Many Colors
In this work we introduce a new type of urn model with infinite but countable many colors indexed by an appropriate infinite set. We mainly consider the indexing set of colors to be the $d$-dimensional integer lattice and consider balanced replacement schemes associated with bounded increment random walks on it. We prove central and local limit theorems for the random color of the $n$-th selected ball and show that irrespective of the null recurrent or transient behavior of the underlying random walks, the asymptotic distribution is Gaussian after appropriate centering and scaling. We show that the order of any non-zero centering is always ${\mathcal O}\left(\log n\right)$ and the scaling is ${\mathcal O}\left(\sqrt{\log n}\right)$. The work also provides similar results for urn models with infinitely many colors indexed by more general lattices in ${\mathbb R}^d$. We introduce a novel technique of representing the random color of the $n$-th selected ball as a suitably sampled point on the path of the underlying random walk. This helps us to derive the central and local limit theorems.