On Bayesian estimation of multinomial probabilities under incomplete experimental information

In this work, we discuss Bayesian estimation of multinomial probabilities associated with a finite alphabet A under incomplete experimental information. Two types of prior information are considered: (i) number of letters needed to see a particular pattern for the first time, and (ii) the fact that for two fixed words one appeared before the other.Patterns, Stopping times, Incomplete experimental information

Non-identifiability of the two state Markovian Arrival process

In this paper we consider the problem of identifiability of the two-state Markovian Arrival process (MAP2). In particular, we show that the MAP2 is not identifiable and conditions are given under which two different sets of parameters, induce identical stationary laws for the observable process.Batch Markovian Arrival process, Markov Renewal process, Hidden Markov models, Identifiability problems

On identifiability of MAP processes

Two types of transitions can be found in the Markovian Arrival process or MAP: with and without arrivals. In transient transitions the chain jumps from one state to another with no arrival; in effective transitions, a single arrival occurs. We assume that in practice, only arrival times are observed in a MAP. This leads us to define and study the Effective Markovian Arrival process or E-MAP. In this work we define identifiability of MAPs in terms of equivalence between the corresponding E-MAPs and study conditions under which two sets of parameters induce identical laws for the observable process, in the case of 2 and 3-states MAP. We illustrate and discuss our results with examples.Batch Markovian Arrival process, Hidden Markov models, Identifiability problems

Inference for double Pareto lognormal queues with applications

In this article we describe a method for carrying out Bayesian inference for the double Pareto lognormal (dPlN) distribution which has recently been proposed as a model for heavy-tailed phenomena. We apply our approach to inference for the dPlN/M/1 and M/dPlN/1 queueing systems. These systems cannot be analyzed using standard techniques due to the fact that the dPlN distribution does not posses a Laplace transform in closed form. This difficulty is overcome using some recent approximations for the Laplace transform for the Pareto/M/1 system. Our procedure is illustrated with applications in internet traffic analysis and risk theory.Heavy tails, Bayesian inference, Queueing theory

StaRAC : Stationnarité Relative et Approches Connexes

International audienc

Stationnarité relative et approches connexes

International audienceThe paper is concerned with the approach developed within the ANR Project StaRAC, and it gives an overview of its main results. The objective was to reconsider the concept of stationarity so as to make it operational, allowing for both an interpretation relatively to an observation scale and the possibility of its testing thanks to the use of time-frequency surrogates, as well as to offer various extensions, especially beyond shift invariance

Book of Abstracts. V International Workshop on Proximity Data, Multivariate Analysis and Classification

Abstracts of the V International Workshop on Proximity Data, Multivariate Analysis and Classification. July 11-12, 2023 (Valladolid, Spain)

Effect of rosiglitazone on the frequency of diabetes in patients with impaired glucose tolerance or impaired fasting glucose: a randomised controlled trial

Background Rosiglitazone is a thiazolidinedione that reduces insulin resistance and might preserve insulin secretion. The aim of this study was to assess prospectively the drugs ability to prevent type 2 diabetes in individuals at high risk of developing the condition