Search results for "statistical model"
showing 10 items of 163 documents
ADME Prediction with KNIME: Development and Validation of a Publicly Available Workflow for the Prediction of Human Oral Bioavailability.
2020
In silico prediction of human oral bioavailability is a relevant tool for the selection of potential drug candidates and for the rejection of those molecules with less probability of success during the early stages of drug discovery and development. However, the high variability and complexity of oral bioavailability and the limited experimental data in the public domain have mainly restricted the development of reliable in silico models to predict this property from the chemical structure. In this study we present a KNIME automated workflow to predict human oral bioavailability of new drug and drug-like molecules based on five machine learning approaches combined into an ensemble model. Th…
Modelling urban networks sustainable progress
2019
In this paper, we analyse the relations between thermodynamics and city networks: an increase in the complexity and the organized information in such urban systems leads to less demand for resources and less social entropy, which overall makes them more efficient and stable. The goal of this study is to propose a method to measuring city networks sustainable progress based on statistical models, derived from Eurostat databases and NASA satellite images, and capable of analyzing different conceptual scenarios of urban development in Europe. The obtained probability-based indices enable us to evaluate the dynamics of city networks in terms of three components of sustainable progress – economi…
Bayesian metanetworks for modelling user preferences in mobile environment
2003
The problem of profiling and filtering is important particularly for mobile information systems where wireless network traffic and mobile terminal’s size are limited comparing to the Internet access from the PC. Dealing with uncertainty in this area is crucial and many researchers apply various probabilistic models. The main challenge of this paper is the multilevel probabilistic model (the Bayesian Metanetwork), which is an extension of traditional Bayesian networks. The extra level(s) in the Metanetwork is used to select the appropriate substructure from the basic network level based on contextual features from user’s profile (e.g. user’s location). Two models of the Metanetwork are consi…
Improving estimation of distribution genetic programming with novelty initialization
2021
Estimation of distribution genetic programming (EDA-GP) replaces the standard variation operations of genetic programming (GP) by learning and sampling from a probabilistic model. Unfortunately, many EDA-GP approaches suffer from a rapidly decreasing population diversity which often leads to premature convergence. However, novelty search, an approach that searches for novel solutions to cover sparse areas of the search space, can be used for generating diverse initial populations. In this work, we propose novelty initialization and test this new method on a generalization of the royal tree problem and compare its performance to ramped half-and-half (RHH) using a recent EDA-GP approach. We f…
What should I do next? Using shared representations to solve interaction problems
2011
Studies on how “the social mind” works reveal that cognitive agents engaged in joint actions actively estimate and influence another’s cognitive variables and form shared representations with them. (How) do shared representations enhance coordination? In this paper, we provide a probabilistic model of joint action that emphasizes how shared representations help solving interaction problems. We focus on two aspects of the model. First, we discuss how shared representations permit to coordinate at the level of cognitive variables (beliefs, intentions, and actions) and determine a coherent unfolding of action execution and predictive processes in the brains of two agents. Second, we discuss th…
Abstraction of covariations in incidental learning and covariation bias
1997
Experiment 1 was devised to distinguish, in a given set of features composing drawn robots, those whose variations were related a priori for participants from those whose variations were a priori independent. In Expt 2, correlations were experimentally induced between a priori-related features for one group of participants (pre-primed group), and between a priori-independent features for another group {arbitrary group), in incidental learning conditions. A subsequent transfer phase revealed that participants' performances were sensitive to experimentally induced correlations in both groups. However, only the performances of the pre-primed group accurately matched the predictions of a statis…
Multi-user interference mitigation under limited feedback requirements for WCDMA systems with base station cooperation
2016
One of the techniques that has been recently identified for dealing with multi-user interference (MUI) in future communications systems is base station (BS) cooperation or joint processing. However, perfect MUI cancellation with this technique demands severe synchronization requirements, perfect and global channel state information (CSI), and an increased backhaul and signaling overhead. In this paper, we consider a more realistic layout with the aim of mitigating the MUI, where only local CSI is available at the BSs. Due to synchronization inaccuracies and errors in the channel estimation, the system becomes partially asynchronous. In the downlink of wideband code division multiple access …
Morphostatistical characterization of the spatial galaxy distribution through Gibbs point processes
2021
This paper proposes a morpho-statistical characterisation of the galaxy distribution through spatial statistical modelling based on inhomogeneous Gibbs point processes. The galaxy distribution is supposed to exhibit two components. The first one is related to the major geometrical features exhibited by the observed galaxy field, here, its corresponding filamentary pattern. The second one is related to the interactions exhibited by the galaxies. Gibbs point processes are statistical models able to integrate these two aspects in a probability density, controlled by some parameters. Several such models are fitted to real observational data via the ABC Shadow algorithm. This algorithm provides …
Inferring slowly-changing dynamic gene-regulatory networks
2015
Dynamic gene-regulatory networks are complex since the interaction patterns between their components mean that it is impossible to study parts of the network in separation. This holistic character of gene-regulatory networks poses a real challenge to any type of modelling. Graphical models are a class of models that connect the network with a conditional independence relationships between random variables. By interpreting these random variables as gene activities and the conditional independence relationships as functional non-relatedness, graphical models have been used to describe gene-regulatory networks. Whereas the literature has been focused on static networks, most time-course experi…
Dealing with preference uncertainty in contingent willingness to pay for a nature protection program: A new approach
2013
In this paper, we propose an alternative preference uncertainty measurement approach where respondents have the option to indicate their willingness to pay (WTP) for a nature protection program either as exact values or intervals from a payment card, depending on whether they are uncertain about their valuation. On the basis of their responses, we then estimate their degree of uncertainty. New within this study is that the respondent's degree of uncertainty is "revealed", while it is "stated" in those using existing measurement methods. Three statistical models are used to explore the sources of respondent uncertainty. We also present a simple way of calculating the uncertainty adjusted mea…