One of the major challenges in distributed systems is establishing consistency among replicated data in a timely fashion. While the consistent ordering of events has been extensively researched, the time span to reach a consistent state is mostly considered an effect of the chosen consistency model, rather than being considered a parameter itself. This paper argues that it is possible to give g...

We consider the problem of learning a Bayesian network structure given n examples and the prior probability based on maximizing the posterior probability. We propose an algorithm that runs in O(n log n) time and that addresses continuous variables and discrete variables without assuming any class of distribution. We prove that the decision is strongly consistent, i.e., correct with probability ...

In the machine learning community, the Bayesian scoring criterion is widely used for model selection problems. One of the fundamental theoretical properties justifying the usage of the Bayesian scoring criterion is its consistency. In this paper we refine this property for the case of binomial Bayesian network models. As a by-product of our derivations we establish strong consistency and obtain...

We consider the high order moments estimator of the frontier of a random pair, introduced by Girard, S., Guillou, A., Stupfler, G. (2013). Frontier estimation with kernel regression on high order moments. In the present paper, we show that this estimator is strongly uniformly consistent on compact sets and its rate of convergence is given when the conditional cumulative distribution function be...

In finite mixtures of location-scale distributions, if there is no constraint on the parameters then the maximum likelihood estimate does not exist. But when the ratios of the scale parameters are restricted appropriately, the maximum likelihood estimate exists. We prove that the maximum likelihood estimator (MLE) is strongly consistent, if the ratios of the scale parameters are restricted from...

This paper reviews several proposed methods for estimating the mode of a probability density function. The method of Chemoff [2] is examined in greater detail and is shown to be a strongly consistent estimate of a somewhat generalized mode under relaxed conditions on the distribution. Finally, an application to determining blood flow characteristics is given. KeywoTds: Mode Es timation of Mode ...

This paper investigates the use of partial replication in the Database State Machine approach introduced earlier for fully replicated databases. It builds on the order and atomicity properties of group communication primitives to achieve strong consistency and proposes two new abstractions: Resilient Atomic Commit and Fast Atomic Broadcast. Even with atomic broadcast, partial replication requir...

Replication technology, such as Quorum NRW system, is the most effective technology to improve availability of SaaS. However, the variety of SaaS transaction will result in application unbalance. In this paper, we construct a new Quorum NRW system for multitenant database which is based on the method of sharing database and schema model. It can provide different R and W values for each tenant a...

We present multivariate penalized least squares regression estimates. We use Vapnik{ Chervonenkis theory and bounds on the covering numbers to analyze convergence of the estimates. We show strong consistency of the truncated versions of the estimates without any conditions on the underlying distribution.

We present a protocol for general state machine replication – a method that provides strong consistency – that has high performance in a wide-area network. In particular, our protocol Mencius has high throughput under high client load and low latency under low client load even under changing wide-area network environment and client load. We develop our protocol as a derivation from the well-kno...