0000000001192590
AUTHOR
Guillaume Lecuelle
showing 5 related works from this author
Modeling TDS data and segmenting consumers thanks to a mixture of semi-Markov processes
2018
International audience
Estimating finite mixtures of semi-Markov chains: an application to the segmentation of temporal sensory data
2019
Summary In food science, it is of great interest to obtain information about the temporal perception of aliments to create new products, to modify existing products or more generally to understand the mechanisms of perception. Temporal dominance of sensations is a technique to measure temporal perception which consists in choosing sequentially attributes describing a food product over tasting. This work introduces new statistical models based on finite mixtures of semi-Markov chains to describe data collected with the temporal dominance of sensations protocol, allowing different temporal perceptions for a same product within a population. The identifiability of the parameters of such mixtur…
Analysis and modeling of Temporal Dominance of Sensations with stochastic processes
2019
Temporal Dominance of Sensations (TDS) is a technique to measure temporal perception of food product during tasting. For a panelist, it consists in choosing in a list of attributes which one is dominant at any time. This work aims to model TDS data with a stochastic process and proposes to use semi-Markov processes (SMP), a generalization of Markov chains which allows dominance durations to be modeled by any type of distribution. The model can then be used to compare TDS samples based on likelihood ratio. Because probabilities of transition from one attribute to another one can also depend on time, we propose to model TDS by period and we propose a method to select optimally the number of p…
Analysis and modeling of Temporal Dominance of Sensations with stochastic processes
2019
Temporal Dominance of Sensations (TDS) is a technique to measure temporal perception of food product during tasting. For a panelist, it consists in choosing in a list of attributes which one is dominant at any time. This work aims to model TDS data with a stochastic process and proposes to use semi-Markov processes (SMP), a generalization of Markov chains which allows dominance durations to be modeled by any type of distribution. The model can then be used to compare TDS samples based on likelihood ratio. Because probabilities of transition from one attribute to another one can also depend on time, we propose to model TDS by period and we propose a method to select optimally the number of p…
Modeling temporal dominance of sensations data with stochastic processes
2018
National audience