Search results for "Markov chain"
showing 10 items of 288 documents
A comprehensive probabilistic analysis of approximate SIR‐type epidemiological models via full randomized discrete‐time Markov chain formulation with…
2020
Spanish Ministerio de Economia y Competitividad, Grant/Award Number: MTM2017-89664-P; Generalitat Valenciana, Grant/Award Number: APOSTD/2019/128; Ministerio de Economia y Competitividad, Grant/Award Number: MTM2017-89664-P
The Bias of combining variables on fish's aggressive behavior studies.
2019
Made available in DSpace on 2019-10-06T16:27:42Z (GMT). No. of bitstreams: 0 Previous issue date: 2019-07-01 Quantifying animal aggressive behavior by behavioral units, either displays or attacks, is a common practice in animal behavior studies. However, this practice can generate a bias in data analysis, especially when the variables have different temporal patterns. This study aims to use Bayesian Hierarchical Linear Models (B-HLMs) to analyze the feasibility of pooling the aggressive behavior variables of four cichlids species. Additionally, this paper discusses the feasibility of combining variables by examining the usage of different sample sizes and family distributions to aggressive …
Accounting for preferential sampling in species distribution models
2019
D. C., A. L. Q. and F. M. would like to thank the Ministerio de Educación y Ciencia (Spain) for financial support (jointly financed by the European Regional Development Fund) via Research Grants MTM2013‐42323‐P and MTM2016‐77501‐P, and ACOMP/2015/202 from Generalitat Valenciana (Spain). Species distribution models (SDMs) are now being widely used in ecology for management and conservation purposes across terrestrial, freshwater, and marine realms. The increasing interest in SDMs has drawn the attention of ecologists to spatial models and, in particular, to geostatistical models, which are used to associate observations of species occurrence or abundance with environmental covariates in a fi…
Bayesian analysis improves experimental studies about temporal patterning of aggression in fish.
2017
Made available in DSpace on 2018-12-11T17:15:13Z (GMT). No. of bitstreams: 0 Previous issue date: 2017-12-01 Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) This study aims to describe a Bayesian Hierarchical Linear Model (HLM) approach for longitudinal designs in fish's experimental aggressive behavior studies as an alternative to classical methods In particular, we discuss the advantages of Bayesian analysis in dealing with combined variables, non-statistically significant results and required sample size using an experiment of angelfish (Pterophyllum scalare) species as case study. Groups of 3 individuals were subjected to daily observations recorded for 10 min durin…
Understanding the coexistence of competing raptors by Markov chain analysis enhances conservation of vulnerable species.
2016
Understanding ecological interactions among protected species is crucial for correct management to avoid conflicting outcomes of conservation planning. The occurrence of a superior competitor may drive the exclusion of a subordinate contestant, as in Sicily where the largest European population of the lanner falcon is declining because of potentially competing with the peregrine falcon. We measured the coexistence of these two ecologically equivalent species through null models and randomization algorithms on body sizes and ecological niche traits. Lanners and peregrines are morphologically very similar (Hutchinson ratios <1.3) and show 99% diet overlap, and both of these results predict …
Hierarchical log Gaussian Cox process for regeneration in uneven-aged forests
2021
We propose a hierarchical log Gaussian Cox process (LGCP) for point patterns, where a set of points x affects another set of points y but not vice versa. We use the model to investigate the effect of large trees to the locations of seedlings. In the model, every point in x has a parametric influence kernel or signal, which together form an influence field. Conditionally on the parameters, the influence field acts as a spatial covariate in the intensity of the model, and the intensity itself is a non-linear function of the parameters. Points outside the observation window may affect the influence field inside the window. We propose an edge correction to account for this missing data. The par…
A Stochastic Routing Algorithm for Distributed IoT with Unreliable Wireless Links
2016
Punctual and reliable transmission of collected information is indispensable for many Internet of Things (IoT) applications. Such applications rely on IoT devices operating over wireless communication links which are intrinsically unreliable. Consequently to improve packet delivery success while reducing delivery delay is a challenging task for data transmission in the IoT. In this paper, we propose an improved distributed stochastic routing algorithm to increase packet delivery ratio and decrease delivery delay in IoT with unreliable communication links. We adopt the concept of absorbing Markov chain to model the network and evaluate the expected delivery ratio and expected delivery delay …
Convergence of direct recursive algorithm for identification of Preisach hysteresis model with stochastic input
2015
We consider a recursive iterative algorithm for identification of parameters of the Preisach model, one of the most commonly used models of hysteretic input-output relationships. The classical identification algorithm due to Mayergoyz defines explicitly a series of test inputs that allow one to find parameters of the Preisach model with any desired precision provided that (a) such input time series can be implemented and applied; and, (b) the corresponding output data can be accurately measured and recorded. Recursive iterative identification schemes suitable for a number of engineering applications have been recently proposed as an alternative to the classical algorithm. These recursive sc…
Prediction-Based Assembly Assistance System
2020
This paper presents the design of a prediction-based assembly assistance system for manual operations and the results obtained on the data collected from experiments of assembling a customizable product. We integrated into the proposed system a Markov predictor improved with a padding mechanism whose role is to recommend the next assembly step and to detect the worker’s errors. The predictor is trained with correct assembly patterns and tested with real assembly/manufacturing data. The proposed predictor improves the coverage and, thus, there is a significantly higher number of assembly steps which are correctly correlated with the real intentions of the workers.
Revealing community structures by ensemble clustering using group diffusion
2018
We propose an ensemble clustering approach using group diffusion to reveal community structures in data. We represent data points as a directed graph and assume each data point belong to single cluster membership instead of multiple memberships. The method is based on the concept of ensemble group diffusion with a parameter to represent diffusion depth in clustering. The ability to modulate the diffusion-depth parameter by varying it within a certain interval allows for more accurate construction of clusters. Depending on the value of the diffusion-depth parameter, the presented approach can determine very well both local clusters and global structure of data. At the same time, the ability …