9 research outputs found
Gibbs Measures with Multilinear Forms
In this paper, we study a class of multilinear Gibbs measures with
Hamiltonian given by a generalized -statistic and with a general
base measure. Expressing the asymptotic free energy as an optimization problem
over a space of functions, we obtain necessary and sufficient conditions for
replica-symmetry. Utilizing this, we obtain weak limits for a large class of
statistics of interest, which includes the ''local fields/magnetization'', the
Hamiltonian, the global magnetization, etc. An interesting consequence is a
universal weak law for contrasts under replica symmetry, namely,
weakly, if . Our
results yield a probabilistic interpretation for the optimizers arising out of
the limiting free energy. We also prove the existence of a sharp phase
transition point in terms of the temperature parameter, thereby generalizing
existing results that were only known for quadratic Hamiltonians. As a
by-product of our proof technique, we obtain exponential concentration bounds
on local and global magnetizations, which are of independent interest.Comment: 44 page
PC Adjusted Testing for Low Dimensional Parameters
In this paper we consider the effect of high dimensional Principal Component
(PC) adjustments while inferring the effects of variables on outcomes. This
problem is particularly motivated by applications in genetic association
studies where one performs PC adjustment to account for population
stratification. We consider simple statistical models to obtain asymptotically
precise understanding of when such PC adjustments are supposed to work in terms
of providing valid tests with controlled Type I errors. We also verify these
results through a class of numerical experiments
Inferences on Mixing Probabilities and Ranking in Mixed-Membership Models
Network data is prevalent in numerous big data applications including
economics and health networks where it is of prime importance to understand the
latent structure of network. In this paper, we model the network using the
Degree-Corrected Mixed Membership (DCMM) model. In DCMM model, for each node
, there exists a membership vector ,
where denotes the weight that node puts in
community . We derive novel finite-sample expansion for the
s which allows us to obtain asymptotic distributions and
confidence interval of the membership mixing probabilities and other related
population quantities. This fills an important gap on uncertainty
quantification on the membership profile. We further develop a ranking scheme
of the vertices based on the membership mixing probabilities on certain
communities and perform relevant statistical inferences. A multiplier bootstrap
method is proposed for ranking inference of individual member's profile with
respect to a given community. The validity of our theoretical results is
further demonstrated by via numerical experiments in both real and synthetic
data examples
Machine learning-based Naive Bayes approach for divulgence of Spam Comment in Youtube station
In the 21st Century, web-based media assumes an indispensable part in the interaction and communication of civilization. As an illustration of web-based media viz. YouTube, Facebook, Twitter, etc., can increase the social regard of a person just as a gathering. Yet, every innovation has its pros as well as cons. In some YouTube channels, a machine-made spam remark is produced on that recordings, moreover, a few phony clients additionally remark a spam comment which creates an adverse effect on that YouTube channel.Β The spam remarks can be distinguished by using AI (artificial intelligence) which is based on different Algorithms namely Naive Bayes, SVM, Random Forest, ANN, etc. The present investigation is focussed on a machine learning-based Naive Bayes classifier ordered methodology for the identification of spam remarks on YouTub
Machine learning-based Naive Bayes approach for divulgence of Spam Comment in Youtube station
In the 21st Century, web-based media assumes an indispensable part in the interaction and communication of civilization. As an illustration of web-based media viz. YouTube, Facebook, Twitter, etc., can increase the social regard of a person just as a gathering. Yet, every innovation has its pros as well as cons. In some YouTube channels, a machine-made spam remark is produced on that recordings, moreover, a few phony clients additionally remark a spam comment which creates an adverse effect on that YouTube channel.Β The spam remarks can be distinguished by using AI (artificial intelligence) which is based on different Algorithms namely Naive Bayes, SVM, Random Forest, ANN, etc. The present investigation is focussed on a machine learning-based Naive Bayes classifier ordered methodology for the identification of spam remarks on YouTub