Sarat Babu Moka

Sarat Babu Moka
Macquarie University · School of Mathematical and Physical Sciences

Doctor of Philosophy

About

16
Publications
1,599
Reads
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87
Citations
Citations since 2016
15 Research Items
87 Citations
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20162017201820192020202120220102030
Introduction
My research interests broadly lie in Applied Probability with a specific focus on problems in Monte Carlo Simulation and Data Science. Some of the keywords associated with my research are Stochastic Geometry, Spatial Point Processes, Bayesian Inference, Perfect Sampling, Unbiased Estimation, Large Deviations Theory, Variance Reduction Techniques, and Queueing Theory.
Additional affiliations
July 2017 - present
The University of Queensland
Position
  • PostDoc Position
July 2008 - May 2010
Indian Space Research Organization
Position
  • Engineer
Education
August 2010 - May 2017
Tata Institute of Fundamental Research
Field of study
  • Applied Probability
July 2006 - May 2008
Indian Institute of Science
Field of study
  • Telecommunicatios
July 2001 - May 2005
Andhra University
Field of study
  • Electronics and Communications

Publications

Publications (16)
Preprint
Full-text available
We consider the problem of best subset selection in linear regression, where the goal is to find for every model size $k$, that subset of $k$ features that best fit the response. This is particularly challenging when the total available number of features is very large compared to the number of data samples. We propose COMBSS, a novel continuous op...
Article
Full-text available
This work introduces and compares approaches for estimating rare-event probabilities related to the number of edges in the random geometric graph on a Poisson point process. In the one-dimensional setting, we derive closed-form expressions for a variety of conditional probabilities related to the number of edges in the random geometric graph and de...
Article
Full-text available
Viral spread is a complicated function of biological properties, the environment, preventative measures such as sanitation and masks, and the rate at which individuals come within physical proximity. It is these last two elements that governments can control through social-distancing directives. However, infection measurements are almost always del...
Preprint
Full-text available
We consider open multi-class queueing networks with general arrival processes, general processing time sequences and Bernoulli routing. The network is assumed to be operating under an arbitrary work-conserving scheduling policy that makes the system stable. We study the variability of flows within the network. Computable expressions for quantifying...
Preprint
Full-text available
This work introduces and compares approaches for estimating rare-event probabilities related to the number of edges in the random geometric graph on a Poisson point process. In the one-dimensional setting, we derive closed-form expressions for a variety of conditional probabilities related to the number of edges in the random geometric graph and de...
Preprint
Full-text available
How do fine modifications to social distancing measures really affect COVID-19 spread? A major problem for health authorities is that we do not know. In an imaginary world, we might develop a harmless biological virus that spreads just like COVID-19, but is traceable via a cheap and reliable diagnosis. By introducing such an imaginary virus into th...
Article
Full-text available
Chromosome arm aneuploidies (CAAs) are pervasive in cancers. However, how they affect cancer development, prognosis and treatment remains largely unknown. Here, we analyse CAA profiles of 23,427 tumours, identifying aspects of tumour evolution including probable orders in which CAAs occur and CAAs predicting tissue-specific metastasis. Both haemato...
Preprint
Full-text available
Many simulation problems require the estimation of a ratio of two expectations. In recent years Monte Carlo estimators have been proposed that can estimate such ratios without bias. We investigate the theoretical properties of such estimators for the estimation of $\beta = 1/\mathbb{E}\, Z$, where $Z \geq 0$. The estimator, $\widehat \beta(w)$, is...
Article
Full-text available
Traditionally, coupling from the past (CFTP) methods are used to generate perfect samples from finite Gibbs hard-sphere models, an important class of spatial point processes, which is a set of spheres with the centers on a bounded region that are distributed as a homogeneous Poisson point process (PPP) conditioned that spheres do not overlap with e...
Preprint
Full-text available
We present a perfect sampling algorithm for Gibbs point processes, based on the partial rejection sampling of Guo et al. (2017). Our particular focus is on pairwise interaction processes, penetrable spheres mixture models and area-interaction processes, with a finite interaction range. For an interaction range 2r of the target process, the proposed...
Preprint
Full-text available
We present a perfect sampling algorithm for Gibbs point processes, based on the partial rejection sampling of Guo et al. (2017). Our particular focus is on pairwise interaction processes, penetrable spheres mixture models and area-interaction processes, with a finite interaction range. For an interaction range $2r$ of the target process, the propos...
Article
Full-text available
We consider the problem of generating perfect samples from a Gibbs point process, a spatial process that is absolutely continuous w.r.t. a Poisson point process. Examples include area-interaction processes, hard-sphere models and Strauss processes. Traditionally, this is addressed using coupling from the past (CFTP) based methods. We consider accep...
Article
We consider spatial marked Poisson arrivals in a Polish space. These arrivals are accepted or lost in a general state dependent manner. The accepted arrivals remain in the system for a random amount of time, where the individual sojourn times are i.i.d. For such systems, we develop semi-closed form expressions for the steady state probabilities tha...

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