Search results for "Identifiability"
showing 10 items of 35 documents
Modelling anaerobic biomass growth kinetics with a substrate threshold concentration.
2004
Abstract Many bacteria have been observed to stop growing below a certain substrate threshold concentration. In this study, a modification of the Monod kinetics expression has been proposed to take into account this substrate threshold concentration observed in bacterial growth. Besides the threshold concentration no additional parameters have been added to the kinetic expression and so, only the substrate threshold concentration and the half-saturation constant have to be estimated for model calibration purposes. Furthermore, for parameter estimation purposes, practical identifiability of this new function has been studied and the results have been satisfactory. The new model has been appl…
A practical protocol for calibration of nutrient removal wastewater treatment models
2011
Activated sludge models can be very useful for designing and managing wastewater treatment plants (WWTPs). However, as with every model, they need to be calibrated for correct and reliable application. Activated sludge model calibration is still a crucial point that needs appropriate guidance. Indeed, although calibration protocols have been developed, the model calibration still represents the main bottleneck to modelling. This paper presents a procedure for the calibration of an activated sludge model based on a comprehensive sensitivity analysis and a novel step-wise Monte Carlo-based calibration of the subset of influential parameters. In the proposed procedure the complex calibration i…
Compartmental analysis of dynamic nuclear medicine data: Models and identifiability
2016
Compartmental models based on tracer mass balance are extensively used in clinical and pre-clinical nuclear medicine in order to obtain quantitative information on tracer metabolism in the biological tissue. This paper is the first of a series of two that deal with the problem of tracer coefficient estimation via compartmental modelling in an inverse problem framework. Specifically, here we discuss the identifiability problem for a general n-dimension compartmental system and provide uniqueness results in the case of two-compartment and three-compartment compartmental models. The second paper will utilize this framework in order to show how non-linear regularization schemes can be applied t…
Closedness Properties in EX-Identification of Recursive Functions
1998
In this paper we investigate in which cases unions of identifiable classes of recursive functions are also necessarily identifiable. We consider identification in the limit with bounds on mindchanges and anomalies. Though not closed under the set union, these identification types still have features resembling closedness. For each of them we find such n that 1) if every union of n - 1 classes out of U1;, . . ., Un is identifiable, so is the union of all n classes; 2) there are such classes U1;, . . ., Un-1 that every union of n-2 classes out of them is identifiable, while the union of n - 1 classes is not. We show that by finding these n we can distinguish which requirements put on the iden…
Unions of identifiable families of languages
1996
This paper deals with the satisfiability of requirements put on the identifiability of unions of language families. We consider identification in the limit from a text with bounds on mindchanges and anomalies. We show that, though these identification types are not closed under the set union, some of them still have features that resemble closedness. To formalize this, we generalize the notion of closedness. Then by establishing “how closed” these identification types are we solve the satisfiability problem.
THE IDENTIFIABILITY ANALYSIS FOR SETTING UP MEASURING CAMPAIGNS FOR INTEGRATED WATER QUALITY MODELLING
2010
Identifiability analysis enables one the quantification of the number of model parameters that can be assessed by calibration with respect to a data set. Such a methodology is based on the appraisal of sensitivity coefficients of the model parameters by means of Monte Carlo runs. By employing the Fisher Information Matrix, the methodology is able to gain insights with respect to the number of model parameters that can be reliably assessed. The paper presents a study where identifiability analysis is used as a tool for the setting up of measuring campaigns for integrated water quality modelling. The analysis has been applied to a real case study characterized by a partially urbanized catchme…
On the effectiveness of vocal imitations and verbal descriptions of sounds
2014
cote interne IRCAM: Lemaitre14b; None / None; International audience; Describing unidentified sounds with words is a frustrating task and vocally imitating them is often a convenient way to address the issue. This article reports on a study that compared the effectiveness of vocal imitations and verbalizations to communicate different referent sounds. The stimuli included mechanical and synthesized sounds and were selected on the basis of participants' confidence in identifying the cause of the sounds, ranging from easy-to-identify to unidentifiable sounds. The study used a selection of vocal imitations and verbalizations deemed adequate descriptions of the referent sounds. These descriptio…
Identifying Causal Effects with the R Package causaleffect
2017
Do-calculus is concerned with estimating the interventional distribution of an action from the observed joint probability distribution of the variables in a given causal structure. All identifiable causal effects can be derived using the rules of do-calculus, but the rules themselves do not give any direct indication whether the effect in question is identifiable or not. Shpitser and Pearl constructed an algorithm for identifying joint interventional distributions in causal models, which contain unobserved variables and induce directed acyclic graphs. This algorithm can be seen as a repeated application of the rules of do-calculus and known properties of probabilities, and it ultimately eit…
Structure Learning in Nested Effects Models
2007
Nested Effects Models (NEMs) are a class of graphical models introduced to analyze the results of gene perturbation screens. NEMs explore noisy subset relations between the high-dimensional outputs of phenotyping studies, e.g., the effects showing in gene expression profiles or as morphological features of the perturbed cell. In this paper we expand the statistical basis of NEMs in four directions. First, we derive a new formula for the likelihood function of a NEM, which generalizes previous results for binary data. Second, we prove model identifiability under mild assumptions. Third, we show that the new formulation of the likelihood allows efficiency in traversing model space. Fourth, we…
On the sign recovery by LASSO, thresholded LASSO and thresholded Basis Pursuit Denoising
2020
Basis Pursuit (BP), Basis Pursuit DeNoising (BPDN), and LASSO are popular methods for identifyingimportant predictors in the high-dimensional linear regression model Y = Xβ + ε. By definition, whenε = 0, BP uniquely recovers β when Xβ = Xb and β different than b implies L1 norm of β is smaller than the L1 norm of b (identifiability condition). Furthermore, LASSO can recover the sign of β only under a much stronger irrepresentability condition. Meanwhile, it is known that the model selection properties of LASSO can be improved by hard-thresholdingits estimates. This article supports these findings by proving that thresholded LASSO, thresholded BPDNand thresholded BP recover the sign of β in …