A Hybrid TTS between Unit Selection and HMM-based TTS under limited data conditions
Phung, Trung-Nghia; Luong, Chi Mai; Akagi, Masato
2013-01-01
The intelligibility of HMM-based TTS can reach that of the original speech. However, HMM-based TTS is far from natural. On the contrary, unit selection TTS is the most-natural sounding TTS currently. However, its intelligibility and naturalness on segmental duration and timing are not stable. Additionally, unit selection needs to store a huge amount of data for concatenation. Recently, hybrid approaches between these two TTS, i.e. the HMM trajectory tiling TTS (HTT), have been studied to take...
HMM_Model-Checker pour la vérification probabiliste HMM_Model ...
African Journals Online (AJOL)
ASSIA
probabiliste –Télescope Hubble. Abstract. Probabilistic verification for embedded systems continues to attract more and more followers in the research community. Given a probabilistic model, a formula of temporal logic, describing a property of a system and an exploration algorithm to check whether the property is satisfied ...
Study on solitary word based on HMM model and Baum-Welch algorithm
Directory of Open Access Journals (Sweden)
Junxia CHEN
Full Text Available This paper introduces the principle of Hidden Markov Model, which is used to describe the Markov process with unknown parameters, is a probability model to describe the statistical properties of the random process. On this basis, designed a solitary word detection experiment based on HMM model, by optimizing the experimental model, Using Baum-Welch algorithm for training the problem of solving the HMM model, HMM model to estimate the parameters of the λ value is found, in this view of mathematics equivalent to other linear prediction coefficient. This experiment in reducing unnecessary HMM training at the same time, reduced the algorithm complexity. In order to test the effectiveness of the Baum-Welch algorithm, The simulation of experimental data, the results show that the algorithm is effective.
Variable length and context-dependent HMM letter form models for Arabic handwritten word recognition
Bianne-Bernard, Anne-Laure; Menasri, Fares; Likforman-Sulem, Laurence; Mokbel, Chafic; Kermorvant, Christopher
2012-01-01
We present in this paper an HMM-based recognizer for the recognition of unconstrained Arabic handwritten words. The recognizer is a context-dependent HMM which considers variable topology and contextual information for a better modeling of writing units. We propose an algorithm to adapt the topology of each HMM to the character to be modeled. For modeling the contextual units, a state-tying process based on decision tree clustering is introduced which significantly reduces the number of parameters. Decision trees are built according to a set of expert-based questions on how characters are written. Questions are divided into global questions yielding larger clusters and precise questions yielding smaller ones. We apply this modeling to the recognition of Arabic handwritten words. Experiments conducted on the OpenHaRT2010 database show that variable length topology and contextual information significantly improves the recognition rate.
The trainable trajectory formation model TD-HMM parameterized for the LIPS 2008 challenge
Bailly , Gérard; Govokhina , Oxana; Breton , Gaspard; Elisei , Frédéric; Savariaux , Christophe
2008-01-01
International audience; We describe here the trainable trajectory formation model that will be used for the LIPS'2008 challenge organized at InterSpeech'2008. It predicts articulatory trajectories of a talking face from phonetic input. It basically uses HMM-based synthesis but asynchrony between acoustic and gestural boundaries - taking for example into account non audible anticipatory gestures - is handled by a phasing model that predicts the delays between the acoustic boundaries of allopho...
Speech-To-Text Conversion STT System Using Hidden Markov Model HMM
Directory of Open Access Journals (Sweden)
Su Myat Mon
2015-06-01
Full Text Available Abstract Speech is an easiest way to communicate with each other. Speech processing is widely used in many applications like security devices household appliances cellular phones ATM machines and computers. The human computer interface has been developed to communicate or interact conveniently for one who is suffering from some kind of disabilities. Speech-to-Text Conversion STT systems have a lot of benefits for the deaf or dumb people and find their applications in our daily lives. In the same way the aim of the system is to convert the input speech signals into the text output for the deaf or dumb students in the educational fields. This paper presents an approach to extract features by using Mel Frequency Cepstral Coefficients MFCC from the speech signals of isolated spoken words. And Hidden Markov Model HMM method is applied to train and test the audio files to get the recognized spoken word. The speech database is created by using MATLAB.Then the original speech signals are preprocessed and these speech samples are extracted to the feature vectors which are used as the observation sequences of the Hidden Markov Model HMM recognizer. The feature vectors are analyzed in the HMM depending on the number of states.
Multiple-F0 tracking based on a high-order HMM model
Chang, Wei-Chen; Su, Alvin W.Y.; Yeh,, Chunghsin; Roebel, Axel; Rodet, Xavier
2008-01-01
cote interne IRCAM: Chang08a; None / None; National audience; This paper is about multiple-F0 tracking and the estimation of the number of harmonic source streams in music sound signals. A source stream is understood as generated from a note played by a musical instrument. A note is described by a hiddenMarkovmodel (HMM) having two states: the attack state and the sustain state. It is proposed to first perform the tracking of F0 candidates using a high-order hidden Markov model, based on a fo...
Accelerated Profile HMM Searches.
Directory of Open Access Journals (Sweden)
Sean R Eddy
2011-10-01
Full Text Available Profile hidden Markov models (profile HMMs and probabilistic inference methods have made important contributions to the theory of sequence database homology search. However, practical use of profile HMM methods has been hindered by the computational expense of existing software implementations. Here I describe an acceleration heuristic for profile HMMs, the "multiple segment Viterbi" (MSV algorithm. The MSV algorithm computes an optimal sum of multiple ungapped local alignment segments using a striped vector-parallel approach previously described for fast Smith/Waterman alignment. MSV scores follow the same statistical distribution as gapped optimal local alignment scores, allowing rapid evaluation of significance of an MSV score and thus facilitating its use as a heuristic filter. I also describe a 20-fold acceleration of the standard profile HMM Forward/Backward algorithms using a method I call "sparse rescaling". These methods are assembled in a pipeline in which high-scoring MSV hits are passed on for reanalysis with the full HMM Forward/Backward algorithm. This accelerated pipeline is implemented in the freely available HMMER3 software package. Performance benchmarks show that the use of the heuristic MSV filter sacrifices negligible sensitivity compared to unaccelerated profile HMM searches. HMMER3 is substantially more sensitive and 100- to 1000-fold faster than HMMER2. HMMER3 is now about as fast as BLAST for protein searches.
HMM Adaptation for child speech synthesis
CSIR Research Space (South Africa)
Govender, Avashna
2015-09-01
Full Text Available Hidden Markov Model (HMM)-based synthesis in combination with speaker adaptation has proven to be an approach that is well-suited for child speech synthesis. This paper describes the development and evaluation of different HMM-based child speech...
Directory of Open Access Journals (Sweden)
Amel Ghouila
Full Text Available Identification of protein domains is a key step for understanding protein function. Hidden Markov Models (HMMs have proved to be a powerful tool for this task. The Pfam database notably provides a large collection of HMMs which are widely used for the annotation of proteins in sequenced organisms. This is done via sequence/HMM comparisons. However, this approach may lack sensitivity when searching for domains in divergent species. Recently, methods for HMM/HMM comparisons have been proposed and proved to be more sensitive than sequence/HMM approaches in certain cases. However, these approaches are usually not used for protein domain discovery at a genome scale, and the benefit that could be expected from their utilization for this problem has not been investigated. Using proteins of P. falciparum and L. major as examples, we investigate the extent to which HMM/HMM comparisons can identify new domain occurrences not already identified by sequence/HMM approaches. We show that although HMM/HMM comparisons are much more sensitive than sequence/HMM comparisons, they are not sufficiently accurate to be used as a standalone complement of sequence/HMM approaches at the genome scale. Hence, we propose to use domain co-occurrence--the general domain tendency to preferentially appear along with some favorite domains in the proteins--to improve the accuracy of the approach. We show that the combination of HMM/HMM comparisons and co-occurrence domain detection boosts protein annotations. At an estimated False Discovery Rate of 5%, it revealed 901 and 1098 new domains in Plasmodium and Leishmania proteins, respectively. Manual inspection of part of these predictions shows that it contains several domain families that were missing in the two organisms. All new domain occurrences have been integrated in the EuPathDomains database, along with the GO annotations that can be deduced.
Directory of Open Access Journals (Sweden)
Totok Chamidy
2016-03-01
Full Text Available Abstract— Speech recognition is a system to transform the spoken word into text. Human voice signals have a very high of variability. Speech signals in the different pronunciation text, also resulting in distinctive speech patterns. This, furthermore, happens if the text is spoken by a speaker who is not the mother tongue of the speakers. For example, text Arabic words spoken by Indonesian speaker. In this study, Mel Frequency cepstral Coeffisients (MFCC feature extraction techniques explored for voice recognition of the Arabic words for Indonesian speakers with data training using Arabian native speakers. Furthermore, features that have been extracted, classified using Hidden Markov Model (HMM. HMM is one of the sound modeling where the voice signal is analyzed and searched the maximum probability value that can be recognized, from the modeling results will be obtained parameters are then used in the word recognition process. Recognized word is a word that has the maximum suitability. The system produces an accuracy by an average of 83.1% for test data sampling frequency of 8,000 Hz, 82.3% for test data sampling frequency of 22050 Hz, 82.2% for test data sampling frequency of 44100 Hz.
Objective measures to improve the selection of training speakers in HMM-based child speech synthesis
CSIR Research Space (South Africa)
Govender, Avashna
2016-12-01
Full Text Available to those of female voices than male voices. Therefore, for the remainder of the study, only the MCD measures were used. TABLE III: Objective measures calculated for speaker inde- pendent models Voice Average MCD [db] Average RMSE logF0 [cent] bdl rms 2....11 68 bdl clb 2.22 31 bdl slt 2.36 36 rms clb 1.79 45 rms slt 2.01 47 clb slt 2.05 16 Since [9] showed that the best initial model for child speech synthesis is a gender-independent model, the hypothesis tested in this study was that the closest female...
Faults Diagnosis for Vibration Signal Based on HMM
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Shao Qiang
2014-02-01
Full Text Available Faults behaviors of automotive engine in running-up stage are shown a multidimensional pattern that evolves as a function of time (called dynamic patterns. It is necessary to identify the type of fault during early running stages of automotive engine for the selection of appropriate operator actions to prevent a more severe situation. In this situation, the Faults diagnosis method based on continuous HMM is proposed. Feature vectors of main FFT spectrum component are extracted from vibration signals and looked up as observation vectors of HMM. Several HMMs which substitute the types of faults in automotive engine vibration system are modeled. Decision-making for faults classification is performed. The results of experiment are shown the proposed method is executable and effective.
HMM filtering and parameter estimation of an electricity spot price model
International Nuclear Information System (INIS)
Erlwein, Christina; Benth, Fred Espen; Mamon, Rogemar
2010-01-01
In this paper we develop a model for electricity spot price dynamics. The spot price is assumed to follow an exponential Ornstein-Uhlenbeck (OU) process with an added compound Poisson process. In this way, the model allows for mean-reversion and possible jumps. All parameters are modulated by a hidden Markov chain in discrete time. They are able to switch between different economic regimes representing the interaction of various factors. Through the application of reference probability technique, adaptive filters are derived, which in turn, provide optimal estimates for the state of the Markov chain and related quantities of the observation process. The EM algorithm is applied to find optimal estimates of the model parameters in terms of the recursive filters. We implement this self-calibrating model on a deseasonalised series of daily spot electricity prices from the Nordic exchange Nord Pool. On the basis of one-step ahead forecasts, we found that the model is able to capture the empirical characteristics of Nord Pool spot prices. (author)
Figmop: a profile HMM to identify genes and bypass troublesome gene models in draft genomes.
Curran, David M; Gilleard, John S; Wasmuth, James D
2014-11-15
Gene models from draft genome assemblies of metazoan species are often incorrect, missing exons or entire genes, particularly for large gene families. Consequently, labour-intensive manual curation is often necessary. We present Figmop (Finding Genes using Motif Patterns) to help with the manual curation of gene families in draft genome assemblies. The program uses a pattern of short sequence motifs to identify putative genes directly from the genome sequence. Using a large gene family as a test case, Figmop was found to be more sensitive and specific than a BLAST-based approach. The visualization used allows the validation of potential genes to be carried out quickly and easily, saving hours if not days from an analysis. Source code of Figmop is freely available for download at https://github.com/dave-the-scientist, implemented in C and Python and is supported on Linux, Unix and MacOSX. curran.dave.m@gmail.com Supplementary data are available at Bioinformatics online. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.
Hidden Neural Networks: A Framework for HMM/NN Hybrids
DEFF Research Database (Denmark)
Riis, Søren Kamaric; Krogh, Anders Stærmose
1997-01-01
This paper presents a general framework for hybrids of hidden Markov models (HMM) and neural networks (NN). In the new framework called hidden neural networks (HNN) the usual HMM probability parameters are replaced by neural network outputs. To ensure a probabilistic interpretation the HNN is nor...... HMMs on TIMIT continuous speech recognition benchmarks. On the task of recognizing five broad phoneme classes an accuracy of 84% is obtained compared to 76% for a standard HMM. Additionally, we report a preliminary result of 69% accuracy on the TIMIT 39 phoneme task......This paper presents a general framework for hybrids of hidden Markov models (HMM) and neural networks (NN). In the new framework called hidden neural networks (HNN) the usual HMM probability parameters are replaced by neural network outputs. To ensure a probabilistic interpretation the HNN...
Appropriate baseline values for HMM-based speech recognition
CSIR Research Space (South Africa)
Barnard, E
2004-11-01
Full Text Available values for HMM-based speech recognition Etienne Gouws, Kobus Wolvaardt, Neil Kleynhans, Etienne Barnard Department of Electrical, Electronic and Computer Engineering University of Pretoria Pretoria, South Africa ebarnard@up.ac.za Abstract A number.... Keywords - Hidden Markov Models (HMM), Feature sets, Mixture models, Pronunciation dictionaries, Monophones, Triphones 1. Introduction There is a growing awareness that Human Language Technolo- gies can play a significant role in bridging the digital...
Learning Pullback HMM Distances.
Cuzzolin, Fabio; Sapienza, Michael
2014-07-01
Recent work in action recognition has exposed the limitations of methods which directly classify local features extracted from spatio-temporal video volumes. In opposition, encoding the actions' dynamics via generative dynamical models has a number of attractive features: however, using all-purpose distances for their classification does not necessarily deliver good results. We propose a general framework for learning distance functions for generative dynamical models, given a training set of labelled videos. The optimal distance function is selected among a family of pullback ones, induced by a parametrised automorphism of the space of models. We focus here on hidden Markov models and their model space, and design an appropriate automorphism there. Experimental results are presented which show how pullback learning greatly improves action recognition performances with respect to base distances.
Directory of Open Access Journals (Sweden)
Han Kyusuk
2011-01-01
Full Text Available This paper introduces novel attack detection approaches on mobile and wireless device security and network which consider temporal relations between internet packets. In this paper we first present a field selection technique using a Genetic Algorithm and generate a Packet-based Mining Association Rule from an original Mining Association Rule for Support Vector Machine in mobile and wireless network environment. Through the preprocessing with PMAR, SVM inputs can account for time variation between packets in mobile and wireless network. Third, we present Gaussian observation Hidden Markov Model to exploit the hidden relationships between packets based on probabilistic estimation. In our G-HMM approach, we also apply G-HMM feature reduction for better initialization. We demonstrate the usefulness of our SVM and G-HMM approaches with GA on MIT Lincoln Lab datasets and a live dataset that we captured on a real mobile and wireless network. Moreover, experimental results are verified by -fold cross-validation test.
Sparsely correlated hidden Markov models with application to genome-wide location studies.
Choi, Hyungwon; Fermin, Damian; Nesvizhskii, Alexey I; Ghosh, Debashis; Qin, Zhaohui S
2013-03-01
Multiply correlated datasets have become increasingly common in genome-wide location analysis of regulatory proteins and epigenetic modifications. Their correlation can be directly incorporated into a statistical model to capture underlying biological interactions, but such modeling quickly becomes computationally intractable. We present sparsely correlated hidden Markov models (scHMM), a novel method for performing simultaneous hidden Markov model (HMM) inference for multiple genomic datasets. In scHMM, a single HMM is assumed for each series, but the transition probability in each series depends on not only its own hidden states but also the hidden states of other related series. For each series, scHMM uses penalized regression to select a subset of the other data series and estimate their effects on the odds of each transition in the given series. Following this, hidden states are inferred using a standard forward-backward algorithm, with the transition probabilities adjusted by the model at each position, which helps retain the order of computation close to fitting independent HMMs (iHMM). Hence, scHMM is a collection of inter-dependent non-homogeneous HMMs, capable of giving a close approximation to a fully multivariate HMM fit. A simulation study shows that scHMM achieves comparable sensitivity to the multivariate HMM fit at a much lower computational cost. The method was demonstrated in the joint analysis of 39 histone modifications, CTCF and RNA polymerase II in human CD4+ T cells. scHMM reported fewer high-confidence regions than iHMM in this dataset, but scHMM could recover previously characterized histone modifications in relevant genomic regions better than iHMM. In addition, the resulting combinatorial patterns from scHMM could be better mapped to the 51 states reported by the multivariate HMM method of Ernst and Kellis. The scHMM package can be freely downloaded from http://sourceforge.net/p/schmm/ and is recommended for use in a linux environment.
DEFF Research Database (Denmark)
Li, Chunjian; Andersen, Søren Vang
2007-01-01
noise. For both models, exact EM algorithms are derived for the joint estimation of all system parameters. The exact EM algorithms are obtainable only by appropriate constraints in the model design, and have better convergence properties than algorithms employing generalized EM algorithm or empirical...... iterative schemes. The proposed methods also enjoy good data efficiency since only second order statistics is involved in the computation. When measurement noise is present, a novel Switching Kalman Smoother is incorporated into the EM algorithm, obtaining optimum nonlinear MMSE estimates of the system...
Hidden Markov Model for Stock Selection
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Nguyet Nguyen
2015-10-01
Full Text Available The hidden Markov model (HMM is typically used to predict the hidden regimes of observation data. Therefore, this model finds applications in many different areas, such as speech recognition systems, computational molecular biology and financial market predictions. In this paper, we use HMM for stock selection. We first use HMM to make monthly regime predictions for the four macroeconomic variables: inflation (consumer price index (CPI, industrial production index (INDPRO, stock market index (S&P 500 and market volatility (VIX. At the end of each month, we calibrate HMM’s parameters for each of these economic variables and predict its regimes for the next month. We then look back into historical data to find the time periods for which the four variables had similar regimes with the forecasted regimes. Within those similar periods, we analyze all of the S&P 500 stocks to identify which stock characteristics have been well rewarded during the time periods and assign scores and corresponding weights for each of the stock characteristics. A composite score of each stock is calculated based on the scores and weights of its features. Based on this algorithm, we choose the 50 top ranking stocks to buy. We compare the performances of the portfolio with the benchmark index, S&P 500. With an initial investment of $100 in December 1999, over 15 years, in December 2014, our portfolio had an average gain per annum of 14.9% versus 2.3% for the S&P 500.
An HMM-Like Dynamic Time Warping Scheme for Automatic Speech Recognition
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Ing-Jr Ding
2014-01-01
Full Text Available In the past, the kernel of automatic speech recognition (ASR is dynamic time warping (DTW, which is feature-based template matching and belongs to the category technique of dynamic programming (DP. Although DTW is an early developed ASR technique, DTW has been popular in lots of applications. DTW is playing an important role for the known Kinect-based gesture recognition application now. This paper proposed an intelligent speech recognition system using an improved DTW approach for multimedia and home automation services. The improved DTW presented in this work, called HMM-like DTW, is essentially a hidden Markov model- (HMM- like method where the concept of the typical HMM statistical model is brought into the design of DTW. The developed HMM-like DTW method, transforming feature-based DTW recognition into model-based DTW recognition, will be able to behave as the HMM recognition technique and therefore proposed HMM-like DTW with the HMM-like recognition model will have the capability to further perform model adaptation (also known as speaker adaptation. A series of experimental results in home automation-based multimedia access service environments demonstrated the superiority and effectiveness of the developed smart speech recognition system by HMM-like DTW.
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Ririn Kusumawati
2016-05-01
In the classification, using Hidden Markov Model, voice signal is analyzed and searched the maximum possible value that can be recognized. The modeling results obtained parameters are used to compare with the sound of Arabic speakers. From the test results' Classification, Hidden Markov Models with Linear Predictive Coding extraction average accuracy of 78.6% for test data sampling frequency of 8,000 Hz, 80.2% for test data sampling frequency of 22050 Hz, 79% for frequencies sampling test data at 44100 Hz.
AdOn HDP-HMM: An Adaptive Online Model for Segmentation and Classification of Sequential Data.
Bargi, Ava; Xu, Richard Yi Da; Piccardi, Massimo
2017-09-21
Recent years have witnessed an increasing need for the automated classification of sequential data, such as activities of daily living, social media interactions, financial series, and others. With the continuous flow of new data, it is critical to classify the observations on-the-fly and without being limited by a predetermined number of classes. In addition, a model should be able to update its parameters in response to a possible evolution in the distributions of the classes. This compelling problem, however, does not seem to have been adequately addressed in the literature, since most studies focus on offline classification over predefined class sets. In this paper, we present a principled solution for this problem based on an adaptive online system leveraging Markov switching models and hierarchical Dirichlet process priors. This adaptive online approach is capable of classifying the sequential data over an unlimited number of classes while meeting the memory and delay constraints typical of streaming contexts. In this paper, we introduce an adaptive ''learning rate'' that is responsible for balancing the extent to which the model retains its previous parameters or adapts to new observations. Experimental results on stationary and evolving synthetic data and two video data sets, TUM Assistive Kitchen and collated Weizmann, show a remarkable performance in terms of segmentation and classification, particularly for sequences from evolutionary distributions and/or those containing previously unseen classes.
HMM_RA: An Improved Method for Alpha-Helical Transmembrane Protein Topology Prediction
Directory of Open Access Journals (Sweden)
Changhui Yan
2008-01-01
Full Text Available α-helical transmembrane (TM proteins play important and diverse functional roles in cells. The ability to predict the topology of these proteins is important for identifying functional sites and inferring function of membrane proteins. This paper presents a Hidden Markov Model (referred to as HMM_RA that can predict the topology of α-helical transmembrane proteins with improved performance. HMM_RA adopts the same structure as the HMMTOP method, which has five modules: inside loop, inside helix tail, membrane helix, outside helix tail and outside loop. Each module consists of one or multiple states. HMM_RA allows using reduced alphabets to encode protein sequences. Thus, each state of HMM_RA is associated with n emission probabilities, where n is the size of the reduced alphabet set. Direct comparisons using two standard data sets show that HMM_RA consistently outperforms HMMTOP and TMHMM in topology prediction. Specifically, on a high-quality data set of 83 proteins, HMM_RA outperforms HMMTOP by up to 7.6% in topology accuracy and 6.4% in α-helices location accuracy. On the same data set, HMM_RA outperforms TMHMM by up to 6.4% in topology accuracy and 2.9% in location accuracy. Comparison also shows that HMM_RA achieves comparable performance as Phobius, a recently published method.
DEFF Research Database (Denmark)
ElSalamouny, Ehab; Nielsen, Mogens; Sassone, Vladimiro
2010-01-01
Probabilistic trust has been adopted as an approach to taking security sensitive decisions in modern global computing environments. Existing probabilistic trust frameworks either assume fixed behaviour for the principals or incorporate the notion of ‘decay' as an ad hoc approach to cope with thei......Probabilistic trust has been adopted as an approach to taking security sensitive decisions in modern global computing environments. Existing probabilistic trust frameworks either assume fixed behaviour for the principals or incorporate the notion of ‘decay' as an ad hoc approach to cope...
A Metastate HMM with Application to Gene Structure Identification in Eukaryotes
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Winters-Hilt Stephen
2010-01-01
Full Text Available We introduce a generalized-clique hidden Markov model (HMM and apply it to gene finding in eukaryotes (C. elegans. We demonstrate a HMM structure identification platform that is novel and robustly-performing in a number of ways. The generalized clique HMM begins by enlarging the primitive hidden states associated with the individual base labels (as exon, intron, or junk to substrings of primitive hidden states, or footprint states, having a minimal length greater than the footprint state length. The emissions are likewise expanded to higher order in the fundamental joint probability that is the basis of the generalized-clique, or "metastate", HMM. We then consider application to eukaryotic gene finding and show how such a metastate HMM improves the strength of coding/noncoding-transition contributions to gene-structure identification. We will describe situations where the coding/noncoding-transition modeling can effectively recapture the exon and intron heavy tail distribution modeling capability as well as manage the exon-start needle-in-the-haystack problem. In analysis of the C. elegans genome we show that the sensitivity and specificity (SN,SP results for both the individual-state and full-exon predictions are greatly enhanced over the standard HMM when using the generalized-clique HMM.
Directory of Open Access Journals (Sweden)
Da Liu
2013-01-01
Full Text Available A combined forecast with weights adaptively selected and errors calibrated by Hidden Markov model (HMM is proposed to model the day-ahead electricity price. Firstly several single models were built to forecast the electricity price separately. Then the validation errors from every individual model were transformed into two discrete sequences: an emission sequence and a state sequence to build the HMM, obtaining a transmission matrix and an emission matrix, representing the forecasting ability state of the individual models. The combining weights of the individual models were decided by the state transmission matrixes in HMM and the best predict sample ratio of each individual among all the models in the validation set. The individual forecasts were averaged to get the combining forecast with the weights obtained above. The residuals of combining forecast were calibrated by the possible error calculated by the emission matrix of HMM. A case study of day-ahead electricity market of Pennsylvania-New Jersey-Maryland (PJM, USA, suggests that the proposed method outperforms individual techniques of price forecasting, such as support vector machine (SVM, generalized regression neural networks (GRNN, day-ahead modeling, and self-organized map (SOM similar days modeling.
Text Dependent Speaker Verification Using Un-supervised HMM-UBM and Temporal GMM-UBM
DEFF Research Database (Denmark)
Sarkar, Achintya Kumar; Tan, Zheng-Hua
2016-01-01
In this paper, we investigate the Hidden Markov Model (HMM) and the temporal Gaussian Mixture Model (GMM) systems based on the Universal Background Model (UBM) concept to capture temporal information of speech for Text Dependent (TD) Speaker Verification (SV). In TD-SV, target speakers...... enrollment. However, the temporal information is not considered in the classical GMM-UBM based TD-SV system. Moreover, no transcription knowledge of the speech is required in the HMM-UBM and temporal GMM-UBM based systems. We also study the fusion of the HMM-UBM, the temporal GMM-UBM and the classical GMM......-UBM systems in SV. We show that the HMM-UBM system yields better performance than the other systems in most cases. Further, fusion of the systems improve the overall speaker verification performance. The results are shown in the different tasks of the RedDots challenge 2016 database....
Speaker-Adaptation for Hybrid HMM-ANN Continuous Speech Recognition System
Neto, Joao; Almeida, Luis; Hochberg, Mike; Martins, Ciro; Nunes, Luis; Renals, Steve; Robinson, Tony
1995-01-01
It is well known that recognition performance degrades significantly when moving from a speaker-dependent to a speaker-independent system. Traditional hidden Markov model (HMM) systems have successfully applied speaker-adaptation approaches to reduce this degradation. In this paper we present and evaluate some techniques for speaker-adaptation of a hybrid HMM-artificial neural network (ANN) continuous speech recognition system. These techniques are applied to a well trained, speaker-independe...
Recognition of Bike-Riding States with HMM Analysis
Thepvilojanapong, Niwat; Sugo, Keiji; Namiki, Yutaka; Tobe, Yoshito
2011-06-01
In this paper, we design and implement an sBike (Sensorized Bike) prototype to support cyclists by recognizing various riding states including going straight, turning right or left, meandering, and stopping. An Android phone, which is equipped with a gyro sensor, an accelerometer, and a GPS receiver, is mounted on the handle of bicycle to collect necessary data for analysis. Hidden Markov model (HMM) is adopted to recognize the riding states from raw sensor data. The experimental results show that the accuracy of recognition is as high as 98%. By knowing the riding states of cyclists, road conditions can be inferred and shared amongst users.
Explorations in the History of Machines and Mechanisms : Proceedings of HMM2012
Ceccarelli, Marco
2012-01-01
This book contains the proceedings of HMM2012, the 4th International Symposium on Historical Developments in the field of Mechanism and Machine Science (MMS). These proceedings cover recent research concerning all aspects of the development of MMS from antiquity until the present and its historiography: machines, mechanisms, kinematics, dynamics, concepts and theories, design methods, collections of methods, collections of models, institutions and biographies.
Liu, Zongzhi; Li, Ao; Schulz, Vincent; Chen, Min; Tuck, David
2010-06-01
Genotyping platforms such as single nucleotide polymorphism (SNP) arrays are powerful tools to study genomic aberrations in cancer samples. Allele specific information from SNP arrays provides valuable information for interpreting copy number variation (CNV) and allelic imbalance including loss-of-heterozygosity (LOH) beyond that obtained from the total DNA signal available from array comparative genomic hybridization (aCGH) platforms. Several algorithms based on hidden Markov models (HMMs) have been designed to detect copy number changes and copy-neutral LOH making use of the allele information on SNP arrays. However heterogeneity in clinical samples, due to stromal contamination and somatic alterations, complicates analysis and interpretation of these data. We have developed MixHMM, a novel hidden Markov model using hidden states based on chromosomal structural aberrations. MixHMM allows CNV detection for copy numbers up to 7 and allows more complete and accurate description of other forms of allelic imbalance, such as increased copy number LOH or imbalanced amplifications. MixHMM also incorporates a novel sample mixing model that allows detection of tumor CNV events in heterogeneous tumor samples, where cancer cells are mixed with a proportion of stromal cells. We validate MixHMM and demonstrate its advantages with simulated samples, clinical tumor samples and a dilution series of mixed samples. We have shown that the CNVs of cancer cells in a tumor sample contaminated with up to 80% of stromal cells can be detected accurately using Illumina BeadChip and MixHMM. The MixHMM is available as a Python package provided with some other useful tools at http://genecube.med.yale.edu:8080/MixHMM.
Directory of Open Access Journals (Sweden)
Zongzhi Liu
Full Text Available BACKGROUND: Genotyping platforms such as single nucleotide polymorphism (SNP arrays are powerful tools to study genomic aberrations in cancer samples. Allele specific information from SNP arrays provides valuable information for interpreting copy number variation (CNV and allelic imbalance including loss-of-heterozygosity (LOH beyond that obtained from the total DNA signal available from array comparative genomic hybridization (aCGH platforms. Several algorithms based on hidden Markov models (HMMs have been designed to detect copy number changes and copy-neutral LOH making use of the allele information on SNP arrays. However heterogeneity in clinical samples, due to stromal contamination and somatic alterations, complicates analysis and interpretation of these data. METHODS: We have developed MixHMM, a novel hidden Markov model using hidden states based on chromosomal structural aberrations. MixHMM allows CNV detection for copy numbers up to 7 and allows more complete and accurate description of other forms of allelic imbalance, such as increased copy number LOH or imbalanced amplifications. MixHMM also incorporates a novel sample mixing model that allows detection of tumor CNV events in heterogeneous tumor samples, where cancer cells are mixed with a proportion of stromal cells. CONCLUSIONS: We validate MixHMM and demonstrate its advantages with simulated samples, clinical tumor samples and a dilution series of mixed samples. We have shown that the CNVs of cancer cells in a tumor sample contaminated with up to 80% of stromal cells can be detected accurately using Illumina BeadChip and MixHMM. AVAILABILITY: The MixHMM is available as a Python package provided with some other useful tools at http://genecube.med.yale.edu:8080/MixHMM.
Hidden Markov Model for Stock Trading
Directory of Open Access Journals (Sweden)
Nguyet Nguyen
2018-03-01
Full Text Available Hidden Markov model (HMM is a statistical signal prediction model, which has been widely used to predict economic regimes and stock prices. In this paper, we introduce the application of HMM in trading stocks (with S&P 500 index being an example based on the stock price predictions. The procedure starts by using four criteria, including the Akaike information, the Bayesian information, the Hannan Quinn information, and the Bozdogan Consistent Akaike Information, in order to determine an optimal number of states for the HMM. The selected four-state HMM is then used to predict monthly closing prices of the S&P 500 index. For this work, the out-of-sample R OS 2 , and some other error estimators are used to test the HMM predictions against the historical average model. Finally, both the HMM and the historical average model are used to trade the S&P 500. The obtained results clearly prove that the HMM outperforms this traditional method in predicting and trading stocks.
A hidden Markov model for investigating recent positive selection through haplotype structure.
Chen, Hua; Hey, Jody; Slatkin, Montgomery
2015-02-01
Recent positive selection can increase the frequency of an advantageous mutant rapidly enough that a relatively long ancestral haplotype will be remained intact around it. We present a hidden Markov model (HMM) to identify such haplotype structures. With HMM identified haplotype structures, a population genetic model for the extent of ancestral haplotypes is then adopted for parameter inference of the selection intensity and the allele age. Simulations show that this method can detect selection under a wide range of conditions and has higher power than the existing frequency spectrum-based method. In addition, it provides good estimate of the selection coefficients and allele ages for strong selection. The method analyzes large data sets in a reasonable amount of running time. This method is applied to HapMap III data for a genome scan, and identifies a list of candidate regions putatively under recent positive selection. It is also applied to several genes known to be under recent positive selection, including the LCT, KITLG and TYRP1 genes in Northern Europeans, and OCA2 in East Asians, to estimate their allele ages and selection coefficients. Copyright © 2014 Elsevier Inc. All rights reserved.
ssHMM: extracting intuitive sequence-structure motifs from high-throughput RNA-binding protein data.
Heller, David; Krestel, Ralf; Ohler, Uwe; Vingron, Martin; Marsico, Annalisa
2017-11-02
RNA-binding proteins (RBPs) play an important role in RNA post-transcriptional regulation and recognize target RNAs via sequence-structure motifs. The extent to which RNA structure influences protein binding in the presence or absence of a sequence motif is still poorly understood. Existing RNA motif finders either take the structure of the RNA only partially into account, or employ models which are not directly interpretable as sequence-structure motifs. We developed ssHMM, an RNA motif finder based on a hidden Markov model (HMM) and Gibbs sampling which fully captures the relationship between RNA sequence and secondary structure preference of a given RBP. Compared to previous methods which output separate logos for sequence and structure, it directly produces a combined sequence-structure motif when trained on a large set of sequences. ssHMM's model is visualized intuitively as a graph and facilitates biological interpretation. ssHMM can be used to find novel bona fide sequence-structure motifs of uncharacterized RBPs, such as the one presented here for the YY1 protein. ssHMM reaches a high motif recovery rate on synthetic data, it recovers known RBP motifs from CLIP-Seq data, and scales linearly on the input size, being considerably faster than MEMERIS and RNAcontext on large datasets while being on par with GraphProt. It is freely available on Github and as a Docker image. © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.
Septic shock prediction for ICU patients via coupled HMM walking on sequential contrast patterns.
Ghosh, Shameek; Li, Jinyan; Cao, Longbing; Ramamohanarao, Kotagiri
2017-02-01
Critical care patient events like sepsis or septic shock in intensive care units (ICUs) are dangerous complications which can cause multiple organ failures and eventual death. Preventive prediction of such events will allow clinicians to stage effective interventions for averting these critical complications. It is widely understood that physiological conditions of patients on variables such as blood pressure and heart rate are suggestive to gradual changes over a certain period of time, prior to the occurrence of a septic shock. This work investigates the performance of a novel machine learning approach for the early prediction of septic shock. The approach combines highly informative sequential patterns extracted from multiple physiological variables and captures the interactions among these patterns via coupled hidden Markov models (CHMM). In particular, the patterns are extracted from three non-invasive waveform measurements: the mean arterial pressure levels, the heart rates and respiratory rates of septic shock patients from a large clinical ICU dataset called MIMIC-II. For baseline estimations, SVM and HMM models on the continuous time series data for the given patients, using MAP (mean arterial pressure), HR (heart rate), and RR (respiratory rate) are employed. Single channel patterns based HMM (SCP-HMM) and multi-channel patterns based coupled HMM (MCP-HMM) are compared against baseline models using 5-fold cross validation accuracies over multiple rounds. Particularly, the results of MCP-HMM are statistically significant having a p-value of 0.0014, in comparison to baseline models. Our experiments demonstrate a strong competitive accuracy in the prediction of septic shock, especially when the interactions between the multiple variables are coupled by the learning model. It can be concluded that the novelty of the approach, stems from the integration of sequence-based physiological pattern markers with the sequential CHMM model to learn dynamic
Directory of Open Access Journals (Sweden)
El Moubtahij Hicham
2017-12-01
Full Text Available This paper presents an analytical approach of an offline handwritten Arabic text recognition system. It is based on the Hidden Markov Models (HMM Toolkit (HTK without explicit segmentation. The first phase is preprocessing, where the data is introduced in the system after quality enhancements. Then, a set of characteristics (features of local densities and features statistics are extracted by using the technique of sliding windows. Subsequently, the resulting feature vectors are injected to the Hidden Markov Model Toolkit (HTK. The simple database âArabic-Numbersâ and IFN/ENIT are used to evaluate the performance of this system. Keywords: Hidden Markov Models (HMM Toolkit (HTK, Sliding windows
Comparison of HMM experts with MLP experts in the Full Combination Multi-Band Approach to Robust ASR
Hagen, Astrid; Morris, Andrew
2000-01-01
In this paper we apply the Full Combination (FC) multi-band approach, which has originally been introduced in the framework of posterior-based HMM/ANN (Hidden Markov Model/Artificial Neural Network) hybrid systems, to systems in which the ANN (or Multilayer Perceptron (MLP)) is itself replaced by a Multi Gaussian HMM (MGM). Both systems represent the most widely used statistical models for robust ASR (automatic speech recognition). It is shown how the FC formula for the likelihood--based MGMs...
An improved segmentation-based HMM learning method for Condition-based Maintenance
International Nuclear Information System (INIS)
Liu, T; Lemeire, J; Cartella, F; Meganck, S
2012-01-01
In the domain of condition-based maintenance (CBM), persistence of machine states is a valid assumption. Based on this assumption, we present an improved Hidden Markov Model (HMM) learning algorithm for the assessment of equipment states. By a good estimation of initial parameters, more accurate learning can be achieved than by regular HMM learning methods which start with randomly chosen initial parameters. It is also better in avoiding getting trapped in local maxima. The data is segmented with a change-point analysis method which uses a combination of cumulative sum charts (CUSUM) and bootstrapping techniques. The method determines a confidence level that a state change happens. After the data is segmented, in order to label and combine the segments corresponding to the same states, a clustering technique is used based on a low-pass filter or root mean square (RMS) values of the features. The segments with their labelled hidden state are taken as 'evidence' to estimate the parameters of an HMM. Then, the estimated parameters are served as initial parameters for the traditional Baum-Welch (BW) learning algorithms, which are used to improve the parameters and train the model. Experiments on simulated and real data demonstrate that both performance and convergence speed is improved.
Improving Naturalness of HMM-Based TTS Trained with Limited Data by Temporal Decomposition
PHUNG, Trung-Nghia; PHAN, Thanh-Son; VU, Thang Tat; LUONG, Mai Chi; AKAGI, Masato
2013-01-01
The most important advantage of HMM-based TTS is its highly intelligible. However, speech synthesized by HMM-based TTS is muffled and far from natural, especially under limited data conditions, which is mainly caused by its over-smoothness. Therefore, the motivation for this paper is to improve the naturalness of HMM-based TTS trained under limited data conditions while preserving its intelligibility. To achieve this motivation, a hybrid TTS between HMM-based TTS and the modified restricted T...
DATA DRIVEN DESIGN OF HMM TOPOLOGY FOR ONLINE HANDWRITING RECOGNITION
Lee, J.L.; Kim, J.; Kim, J.H.
2004-01-01
Although HMM is widely used for online handwriting recognition, there is no simple and wellestablished way of designing the HMM topology. We propose a datadriven systematic method to design HMM topology. Data samples in a single pattern class are structurally simplified into a sequence of
Chae, Minho; Danko, Charles G; Kraus, W Lee
2015-07-16
Global run-on coupled with deep sequencing (GRO-seq) provides extensive information on the location and function of coding and non-coding transcripts, including primary microRNAs (miRNAs), long non-coding RNAs (lncRNAs), and enhancer RNAs (eRNAs), as well as yet undiscovered classes of transcripts. However, few computational tools tailored toward this new type of sequencing data are available, limiting the applicability of GRO-seq data for identifying novel transcription units. Here, we present groHMM, a computational tool in R, which defines the boundaries of transcription units de novo using a two state hidden-Markov model (HMM). A systematic comparison of the performance between groHMM and two existing peak-calling methods tuned to identify broad regions (SICER and HOMER) favorably supports our approach on existing GRO-seq data from MCF-7 breast cancer cells. To demonstrate the broader utility of our approach, we have used groHMM to annotate a diverse array of transcription units (i.e., primary transcripts) from four GRO-seq data sets derived from cells representing a variety of different human tissue types, including non-transformed cells (cardiomyocytes and lung fibroblasts) and transformed cells (LNCaP and MCF-7 cancer cells), as well as non-mammalian cells (from flies and worms). As an example of the utility of groHMM and its application to questions about the transcriptome, we show how groHMM can be used to analyze cell type-specific enhancers as defined by newly annotated enhancer transcripts. Our results show that groHMM can reveal new insights into cell type-specific transcription by identifying novel transcription units, and serve as a complete and useful tool for evaluating functional genomic elements in cells.
Bearing Performance Degradation Assessment Using Linear Discriminant Analysis and Coupled HMM
International Nuclear Information System (INIS)
Liu, T; Chen, J; Zhou, X N; Xiao, W B
2012-01-01
Bearing is one of the most important units in rotary machinery, its performance may vary significantly under different working stages. Thus it is critical to choose the most effective features for bearing performance degradation prediction. Linear Discriminant Analysis (LDA) is a useful method in finding few feature's dimensions that best discriminate a set of features extracted from original vibration signals. Another challenge in bearing performance degradation is how to build a model to recognize the different conditions with the data coming from different monitoring channels. In this paper, coupled hidden Markov models (CHMM) is presented to model interacting processes which can overcome the defections of the HMM. Because the input data in CHMM are collected by several sensors, and the interacting information can be fused by coupled modalities, it is more effective than HMM which used only one state chain. The model can be used in estimating the bearing performance degradation states according to several observation data. When becoming degradation pattern recognition, the new observation features should be input into the pre-trained CHMM and calculate the performance index (PI) of the outputs, the changing of PI could be used to describe the different degradation level of the bearings. The results show that PI will decline with the increase of the bearing degradation. Assessment results of the whole life time experimental bearing signals validate the feasibility and effectiveness of this method.
Online adaptive learning of Left-Right Continuous HMM for bearings condition assessment
International Nuclear Information System (INIS)
Cartella, F; Liu, T; Meganck, S; Lemeire, J; Sahli, H
2012-01-01
Standard Hidden Markov Models (HMMs) approaches used for condition assessment of bearings assume that all the possible system states are fixed and known a priori and that training data from all of the associated states are available. Moreover, the training procedure is performed offline, and only once at the beginning, with the available training set. These assumptions significantly impede component diagnosis applications when all of the possible states of the system are not known in advance or environmental factors or operative conditions change during the tool's usage. The method introduced in this paper overcomes the above limitations and proposes an approach to detect unknown degradation modalities using a Left-Right Continuous HMM with a variable state space. The proposed HMM is combined with Change Point Detection algorithms to (i) estimate, from historical observations, the initial number of the model's states, as well as to perform an initial guess of the parameters, and (ii) to adaptively recognize new states and, consequently, adjust the model parameters during monitoring. The approach has been tested using real monitoring data taken from the NASA benchmark repository. A comparative study with state of the art techniques shows improvements in terms of reduction of the training procedure iterations, and early detection of unknown states.
Regularized Speaker Adaptation of KL-HMM for Dysarthric Speech Recognition
Kim, Myungjong; Kim, Younggwan; Yoo, Joohong; Wang, Jun; Kim, Hoirin
2017-01-01
This paper addresses the problem of recognizing the speech uttered by patients with dysarthria, which is a motor speech disorder impeding the physical production of speech. Patients with dysarthria have articulatory limitation, and therefore, they often have trouble in pronouncing certain sounds, resulting in undesirable phonetic variation. Modern automatic speech recognition systems designed for regular speakers are ineffective for dysarthric sufferers due to the phonetic variation. To capture the phonetic variation, Kullback-Leibler divergence based hidden Markov model (KL-HMM) is adopted, where the emission probability of state is parametrized by a categorical distribution using phoneme posterior probabilities obtained from a deep neural network-based acoustic model. To further reflect speaker-specific phonetic variation patterns, a speaker adaptation method based on a combination of L2 regularization and confusion-reducing regularization which can enhance discriminability between categorical distributions of KL-HMM states while preserving speaker-specific information is proposed. Evaluation of the proposed speaker adaptation method on a database of several hundred words for 30 speakers consisting of 12 mildly dysarthric, 8 moderately dysarthric, and 10 non-dysarthric control speakers showed that the proposed approach significantly outperformed the conventional deep neural network based speaker adapted system on dysarthric as well as non-dysarthric speech. PMID:28320669
Estimation of Phoneme-Specific HMM Topologies for the Automatic Recognition of Dysarthric Speech
Directory of Open Access Journals (Sweden)
Santiago-Omar Caballero-Morales
2013-01-01
Full Text Available Dysarthria is a frequently occurring motor speech disorder which can be caused by neurological trauma, cerebral palsy, or degenerative neurological diseases. Because dysarthria affects phonation, articulation, and prosody, spoken communication of dysarthric speakers gets seriously restricted, affecting their quality of life and confidence. Assistive technology has led to the development of speech applications to improve the spoken communication of dysarthric speakers. In this field, this paper presents an approach to improve the accuracy of HMM-based speech recognition systems. Because phonatory dysfunction is a main characteristic of dysarthric speech, the phonemes of a dysarthric speaker are affected at different levels. Thus, the approach consists in finding the most suitable type of HMM topology (Bakis, Ergodic for each phoneme in the speaker’s phonetic repertoire. The topology is further refined with a suitable number of states and Gaussian mixture components for acoustic modelling. This represents a difference when compared with studies where a single topology is assumed for all phonemes. Finding the suitable parameters (topology and mixtures components is performed with a Genetic Algorithm (GA. Experiments with a well-known dysarthric speech database showed statistically significant improvements of the proposed approach when compared with the single topology approach, even for speakers with severe dysarthria.
Cluster-Based Adaptation Using Density Forest for HMM Phone Recognition
DEFF Research Database (Denmark)
Abou-Zleikha, Mohamed; Tan, Zheng-Hua; Christensen, Mads Græsbøll
2014-01-01
the data of each leaf (cluster) in each tree with the corresponding GMM adapted by the leaf data using the MAP method. The results show that the proposed approach achieves 3:8% (absolute) lower phone error rate compared with the standard HMM/GMM and 0:8% (absolute) lower PER compared with bagged HMM/GMM....
International Nuclear Information System (INIS)
Martin Llorente, F.
1990-01-01
The models of atmospheric pollutants dispersion are based in mathematic algorithms that describe the transport, diffusion, elimination and chemical reactions of atmospheric contaminants. These models operate with data of contaminants emission and make an estimation of quality air in the area. This model can be applied to several aspects of atmospheric contamination
Bogiages, Christopher A.; Lotter, Christine
2011-01-01
In their research, scientists generate, test, and modify scientific models. These models can be shared with others and demonstrate a scientist's understanding of how the natural world works. Similarly, students can generate and modify models to gain a better understanding of the content, process, and nature of science (Kenyon, Schwarz, and Hug…
A Proposed Method for Pattern Classification with HMM in the Context of Supervised Learning
Directory of Open Access Journals (Sweden)
Ştefan Gheorghe Pentiuc
2015-03-01
Full Text Available HMM was used in solving difficult problems in pattern recognition such as speech recognition, handwriting, postures and gestures recognition. This paper presents a proposed method based on HMM for a more general problem of pattern recognition, namely one in which the patterns are represented by points in a space with p dimensions. The first part of the paper is focused on the main elements of HMM theory. The following provides a formalism for the automatic classification of patterns in the context of supervised learning and the method by which this can be solved with HMM. The proposal of methodology is validated using GPL Jahmm library that has been extended with a new class for evaluating the performances of pattern classification with HMM.
Schmidt-Eisenlohr, F.; Puñal, O.; Klagges, K.; Kirsche, M.
Apart from the general issue of modeling the channel, the PHY and the MAC of wireless networks, there are specific modeling assumptions that are considered for different systems. In this chapter we consider three specific wireless standards and highlight modeling options for them. These are IEEE 802.11 (as example for wireless local area networks), IEEE 802.16 (as example for wireless metropolitan networks) and IEEE 802.15 (as example for body area networks). Each section on these three systems discusses also at the end a set of model implementations that are available today.
Marchenko, Yulia V.
2012-03-01
Sample selection arises often in practice as a result of the partial observability of the outcome of interest in a study. In the presence of sample selection, the observed data do not represent a random sample from the population, even after controlling for explanatory variables. That is, data are missing not at random. Thus, standard analysis using only complete cases will lead to biased results. Heckman introduced a sample selection model to analyze such data and proposed a full maximum likelihood estimation method under the assumption of normality. The method was criticized in the literature because of its sensitivity to the normality assumption. In practice, data, such as income or expenditure data, often violate the normality assumption because of heavier tails. We first establish a new link between sample selection models and recently studied families of extended skew-elliptical distributions. Then, this allows us to introduce a selection-t (SLt) model, which models the error distribution using a Student\\'s t distribution. We study its properties and investigate the finite-sample performance of the maximum likelihood estimators for this model. We compare the performance of the SLt model to the conventional Heckman selection-normal (SLN) model and apply it to analyze ambulatory expenditures. Unlike the SLNmodel, our analysis using the SLt model provides statistical evidence for the existence of sample selection bias in these data. We also investigate the performance of the test for sample selection bias based on the SLt model and compare it with the performances of several tests used with the SLN model. Our findings indicate that the latter tests can be misleading in the presence of heavy-tailed data. © 2012 American Statistical Association.
Semisupervised learning of hidden Markov models via a homotopy method.
Ji, Shihao; Watson, Layne T; Carin, Lawrence
2009-02-01
Hidden Markov model (HMM) classifier design is considered for the analysis of sequential data, incorporating both labeled and unlabeled data for training; the balance between the use of labeled and unlabeled data is controlled by an allocation parameter \\lambda \\in [0, 1), where \\lambda = 0 corresponds to purely supervised HMM learning (based only on the labeled data) and \\lambda = 1 corresponds to unsupervised HMM-based clustering (based only on the unlabeled data). The associated estimation problem can typically be reduced to solving a set of fixed-point equations in the form of a "natural-parameter homotopy." This paper applies a homotopy method to track a continuous path of solutions, starting from a local supervised solution (\\lambda = 0) to a local unsupervised solution (\\lambda = 1). The homotopy method is guaranteed to track with probability one from \\lambda = 0 to \\lambda = 1 if the \\lambda = 0 solution is unique; this condition is not satisfied for the HMM since the maximum likelihood supervised solution (\\lambda = 0) is characterized by many local optima. A modified form of the homotopy map for HMMs assures a track from \\lambda = 0 to \\lambda = 1. Following this track leads to a formulation for selecting \\lambda \\in [0, 1) for a semisupervised solution and it also provides a tool for selection from among multiple local-optimal supervised solutions. The results of applying the proposed method to measured and synthetic sequential data verify its robustness and feasibility compared to the conventional EM approach for semisupervised HMM training.
Voter models with heterozygosity selection
Czech Academy of Sciences Publication Activity Database
Sturm, A.; Swart, Jan M.
2008-01-01
Roč. 18, č. 1 (2008), s. 59-99 ISSN 1050-5164 R&D Projects: GA ČR GA201/06/1323; GA ČR GA201/07/0237 Institutional research plan: CEZ:AV0Z10750506 Keywords : Heterozygosity selection * rebellious voter model * branching * annihilation * survival * coexistence Subject RIV: BA - General Mathematics Impact factor: 1.285, year: 2008
International Nuclear Information System (INIS)
Zhou Yunlong; Zhang Xueqing; Gao Yunpeng; Cheng Yue
2009-01-01
For studying flow regimes of gas/liquid two-phase in a vertical upward pipe, the conductance fluctuation information of four typical flow regimes was collected by a measuring the system with self-made multiple conductivity probes. Owing to the non-stationarity of conductance fluctuation signals of gas-liquid two-phase flow, a kind of' flow regime identification method based on wavelet packet Multi-scale Information Entropy and Hidden Markov Model (HMM) was put forward. First of all, the collected conductance fluctuation signals were decomposed into eight different frequency bands signals. Secondly, the wavelet packet multi-scale information entropy of different frequency bands signals were regarded as the input characteristic vectors of all states HMM which had been trained. In the end the regime identification of' the gas-liquid two-phase flow could be performed. The study showed that the method that HMM was applied to identify the flow regime was superior to the one that BP neural network was used, and the results proved that the method was efficient and feasible. (authors)
Fragment-HMM: A new approach to protein structure prediction
Li, Shuai Cheng; Bu, Dongbo; Xu, Jinbo; Li, Ming
2008-01-01
We designed a simple position-specific hidden Markov model to predict protein structure. Our new framework naturally repeats itself to converge to a final target, conglomerating fragment assembly, clustering, target selection, refinement, and consensus, all in one process. Our initial implementation of this theory converges to within 6 Å of the native structures for 100% of decoys on all six standard benchmark proteins used in ROSETTA (discussed by Simons and colleagues in a recent paper), which achieved only 14%–94% for the same data. The qualities of the best decoys and the final decoys our theory converges to are also notably better. PMID:18723665
HMMEditor: a visual editing tool for profile hidden Markov model
Directory of Open Access Journals (Sweden)
Cheng Jianlin
2008-03-01
Full Text Available Abstract Background Profile Hidden Markov Model (HMM is a powerful statistical model to represent a family of DNA, RNA, and protein sequences. Profile HMM has been widely used in bioinformatics research such as sequence alignment, gene structure prediction, motif identification, protein structure prediction, and biological database search. However, few comprehensive, visual editing tools for profile HMM are publicly available. Results We develop a visual editor for profile Hidden Markov Models (HMMEditor. HMMEditor can visualize the profile HMM architecture, transition probabilities, and emission probabilities. Moreover, it provides functions to edit and save HMM and parameters. Furthermore, HMMEditor allows users to align a sequence against the profile HMM and to visualize the corresponding Viterbi path. Conclusion HMMEditor provides a set of unique functions to visualize and edit a profile HMM. It is a useful tool for biological sequence analysis and modeling. Both HMMEditor software and web service are freely available.
Model selection for univariable fractional polynomials.
Royston, Patrick
2017-07-01
Since Royston and Altman's 1994 publication ( Journal of the Royal Statistical Society, Series C 43: 429-467), fractional polynomials have steadily gained popularity as a tool for flexible parametric modeling of regression relationships. In this article, I present fp_select, a postestimation tool for fp that allows the user to select a parsimonious fractional polynomial model according to a closed test procedure called the fractional polynomial selection procedure or function selection procedure. I also give a brief introduction to fractional polynomial models and provide examples of using fp and fp_select to select such models with real data.
Fang, Hongqing; He, Lei; Si, Hao; Liu, Peng; Xie, Xiaolei
2014-09-01
In this paper, Back-propagation(BP) algorithm has been used to train the feed forward neural network for human activity recognition in smart home environments, and inter-class distance method for feature selection of observed motion sensor events is discussed and tested. And then, the human activity recognition performances of neural network using BP algorithm have been evaluated and compared with other probabilistic algorithms: Naïve Bayes(NB) classifier and Hidden Markov Model(HMM). The results show that different feature datasets yield different activity recognition accuracy. The selection of unsuitable feature datasets increases the computational complexity and degrades the activity recognition accuracy. Furthermore, neural network using BP algorithm has relatively better human activity recognition performances than NB classifier and HMM. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.
HMMBinder: DNA-Binding Protein Prediction Using HMM Profile Based Features
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Rianon Zaman
2017-01-01
Full Text Available DNA-binding proteins often play important role in various processes within the cell. Over the last decade, a wide range of classification algorithms and feature extraction techniques have been used to solve this problem. In this paper, we propose a novel DNA-binding protein prediction method called HMMBinder. HMMBinder uses monogram and bigram features extracted from the HMM profiles of the protein sequences. To the best of our knowledge, this is the first application of HMM profile based features for the DNA-binding protein prediction problem. We applied Support Vector Machines (SVM as a classification technique in HMMBinder. Our method was tested on standard benchmark datasets. We experimentally show that our method outperforms the state-of-the-art methods found in the literature.
Selected sports talent development models
Michal Vičar
2017-01-01
Background: Sports talent in the Czech Republic is generally viewed as a static, stable phenomena. It stands in contrast with widespread praxis carried out in Anglo-Saxon countries that emphasise its fluctuant nature. This is reflected in the current models describing its development. Objectives: The aim is to introduce current models of talent development in sport. Methods: Comparison and analysing of the following models: Balyi - Long term athlete development model, Côté - Developmen...
Research study on harmonized molecular materials (HMM); Bunshi kyocho zairyo ni kansuru chosa kenkyu
Energy Technology Data Exchange (ETDEWEB)
NONE
1997-03-01
As functional material to satisfy various needs for environmental harmonization and efficient conversion for information-oriented and aging societies, HMM were surveyed. Living bodies effectively carry out transmission/processing of information, and transport/conversion of substances, and these functions are based on harmonization between organic molecules, and between those and metal or inorganic ones. HMM is a key substance to artificially realize these bio-related functions. Its R & D aims at (1) Making a breakthrough in production process based on innovation of material separation/conversion technology, (2) Contribution to an information-oriented society by high-efficiency devices, and (3) Growth of a functional bio-material industry. HMM is classified into three categories: (1) Assembly materials such as organic ultra-thin films (LB film, self-organizing film), and organic/inorganic hybrid materials for optoelectronics, sensors and devices, (2) Mesophase materials such as functional separation membrane and photo-conductive material, and (3) Microporous materials such as synthetic catalyst using guest/host materials. 571 refs., 88 figs., 21 tabs.
MODEL SELECTION FOR SPECTROPOLARIMETRIC INVERSIONS
International Nuclear Information System (INIS)
Asensio Ramos, A.; Manso Sainz, R.; Martínez González, M. J.; Socas-Navarro, H.; Viticchié, B.; Orozco Suárez, D.
2012-01-01
Inferring magnetic and thermodynamic information from spectropolarimetric observations relies on the assumption of a parameterized model atmosphere whose parameters are tuned by comparison with observations. Often, the choice of the underlying atmospheric model is based on subjective reasons. In other cases, complex models are chosen based on objective reasons (for instance, the necessity to explain asymmetries in the Stokes profiles) but it is not clear what degree of complexity is needed. The lack of an objective way of comparing models has, sometimes, led to opposing views of the solar magnetism because the inferred physical scenarios are essentially different. We present the first quantitative model comparison based on the computation of the Bayesian evidence ratios for spectropolarimetric observations. Our results show that there is not a single model appropriate for all profiles simultaneously. Data with moderate signal-to-noise ratios (S/Ns) favor models without gradients along the line of sight. If the observations show clear circular and linear polarization signals above the noise level, models with gradients along the line are preferred. As a general rule, observations with large S/Ns favor more complex models. We demonstrate that the evidence ratios correlate well with simple proxies. Therefore, we propose to calculate these proxies when carrying out standard least-squares inversions to allow for model comparison in the future.
VEMAP 1: Selected Model Results
National Aeronautics and Space Administration — The Vegetation/Ecosystem Modeling and Analysis Project (VEMAP) was a multi-institutional, international effort addressing the response of biogeography and...
The linear utility model for optimal selection
Mellenbergh, Gideon J.; van der Linden, Willem J.
A linear utility model is introduced for optimal selection when several subpopulations of applicants are to be distinguished. Using this model, procedures are described for obtaining optimal cutting scores in subpopulations in quota-free as well as quota-restricted selection situations. The cutting
Directory of Open Access Journals (Sweden)
Neng-Sheng Pai
2014-01-01
Full Text Available This paper applied speech recognition and RFID technologies to develop an omni-directional mobile robot into a robot with voice control and guide introduction functions. For speech recognition, the speech signals were captured by short-time processing. The speaker first recorded the isolated words for the robot to create speech database of specific speakers. After the speech pre-processing of this speech database, the feature parameters of cepstrum and delta-cepstrum were obtained using linear predictive coefficient (LPC. Then, the Hidden Markov Model (HMM was used for model training of the speech database, and the Viterbi algorithm was used to find an optimal state sequence as the reference sample for speech recognition. The trained reference model was put into the industrial computer on the robot platform, and the user entered the isolated words to be tested. After processing by the same reference model and comparing with previous reference model, the path of the maximum total probability in various models found using the Viterbi algorithm in the recognition was the recognition result. Finally, the speech recognition and RFID systems were achieved in an actual environment to prove its feasibility and stability, and implemented into the omni-directional mobile robot.
VEMAP 1: Selected Model Results
National Aeronautics and Space Administration — ABSTRACT: The Vegetation/Ecosystem Modeling and Analysis Project (VEMAP) was a multi-institutional, international effort addressing the response of biogeography and...
Exploring Several Methods of Groundwater Model Selection
Samani, Saeideh; Ye, Ming; Asghari Moghaddam, Asghar
2017-04-01
Selecting reliable models for simulating groundwater flow and solute transport is essential to groundwater resources management and protection. This work is to explore several model selection methods for avoiding over-complex and/or over-parameterized groundwater models. We consider six groundwater flow models with different numbers (6, 10, 10, 13, 13 and 15) of model parameters. These models represent alternative geological interpretations, recharge estimates, and boundary conditions at a study site in Iran. The models were developed with Model Muse, and calibrated against observations of hydraulic head using UCODE. Model selection was conducted by using the following four approaches: (1) Rank the models using their root mean square error (RMSE) obtained after UCODE-based model calibration, (2) Calculate model probability using GLUE method, (3) Evaluate model probability using model selection criteria (AIC, AICc, BIC, and KIC), and (4) Evaluate model weights using the Fuzzy Multi-Criteria-Decision-Making (MCDM) approach. MCDM is based on the fuzzy analytical hierarchy process (AHP) and fuzzy technique for order performance, which is to identify the ideal solution by a gradual expansion from the local to the global scale of model parameters. The KIC and MCDM methods are superior to other methods, as they consider not only the fit between observed and simulated data and the number of parameter, but also uncertainty in model parameters. Considering these factors can prevent from occurring over-complexity and over-parameterization, when selecting the appropriate groundwater flow models. These methods selected, as the best model, one with average complexity (10 parameters) and the best parameter estimation (model 3).
Selection of classification models from repository of model for water ...
African Journals Online (AJOL)
This paper proposes a new technique, Model Selection Technique (MST) for selection and ranking of models from the repository of models by combining three performance measures (Acc, TPR and TNR). This technique provides weightage to each performance measure to find the most suitable model from the repository of ...
HMM Adaptation for Improving a Human Activity Recognition System
Directory of Open Access Journals (Sweden)
Rubén San-Segundo
2016-09-01
Full Text Available When developing a fully automatic system for evaluating motor activities performed by a person, it is necessary to segment and recognize the different activities in order to focus the analysis. This process must be carried out by a Human Activity Recognition (HAR system. This paper proposes a user adaptation technique for improving a HAR system based on Hidden Markov Models (HMMs. This system segments and recognizes six different physical activities (walking, walking upstairs, walking downstairs, sitting, standing and lying down using inertial signals from a smartphone. The system is composed of a feature extractor for obtaining the most relevant characteristics from the inertial signals, a module for training the six HMMs (one per activity, and the last module for segmenting new activity sequences using these models. The user adaptation technique consists of a Maximum A Posteriori (MAP approach that adapts the activity HMMs to the user, using some activity examples from this specific user. The main results on a public dataset have reported a significant relative error rate reduction of more than 30%. In conclusion, adapting a HAR system to the user who is performing the physical activities provides significant improvement in the system’s performance.
An Efficient Algorithm for Modelling Duration in Hidden Markov Models, with a Dramatic Application
DEFF Research Database (Denmark)
Hauberg, Søren; Sloth, Jakob
2008-01-01
For many years, the hidden Markov model (HMM) has been one of the most popular tools for analysing sequential data. One frequently used special case is the left-right model, in which the order of the hidden states is known. If knowledge of the duration of a state is available it is not possible...... to represent it explicitly with an HMM. Methods for modelling duration with HMM's do exist (Rabiner in Proc. IEEE 77(2):257---286, [1989]), but they come at the price of increased computational complexity. Here we present an efficient and robust algorithm for modelling duration in HMM's, and this algorithm...
A Dynamic Model for Limb Selection
Cox, R.F.A; Smitsman, A.W.
2008-01-01
Two experiments and a model on limb selection are reported. In Experiment 1 left-handed and right-handed participants (N = 36) repeatedly used one hand for grasping a small cube. After a clear switch in the cube’s location, perseverative limb selection was revealed in both handedness groups. In
Review and selection of unsaturated flow models
Energy Technology Data Exchange (ETDEWEB)
Reeves, M.; Baker, N.A.; Duguid, J.O. [INTERA, Inc., Las Vegas, NV (United States)
1994-04-04
Since the 1960`s, ground-water flow models have been used for analysis of water resources problems. In the 1970`s, emphasis began to shift to analysis of waste management problems. This shift in emphasis was largely brought about by site selection activities for geologic repositories for disposal of high-level radioactive wastes. Model development during the 1970`s and well into the 1980`s focused primarily on saturated ground-water flow because geologic repositories in salt, basalt, granite, shale, and tuff were envisioned to be below the water table. Selection of the unsaturated zone at Yucca Mountain, Nevada, for potential disposal of waste began to shift model development toward unsaturated flow models. Under the US Department of Energy (DOE), the Civilian Radioactive Waste Management System Management and Operating Contractor (CRWMS M&O) has the responsibility to review, evaluate, and document existing computer models; to conduct performance assessments; and to develop performance assessment models, where necessary. This document describes the CRWMS M&O approach to model review and evaluation (Chapter 2), and the requirements for unsaturated flow models which are the bases for selection from among the current models (Chapter 3). Chapter 4 identifies existing models, and their characteristics. Through a detailed examination of characteristics, Chapter 5 presents the selection of models for testing. Chapter 6 discusses the testing and verification of selected models. Chapters 7 and 8 give conclusions and make recommendations, respectively. Chapter 9 records the major references for each of the models reviewed. Appendix A, a collection of technical reviews for each model, contains a more complete list of references. Finally, Appendix B characterizes the problems used for model testing.
Review and selection of unsaturated flow models
International Nuclear Information System (INIS)
Reeves, M.; Baker, N.A.; Duguid, J.O.
1994-01-01
Since the 1960's, ground-water flow models have been used for analysis of water resources problems. In the 1970's, emphasis began to shift to analysis of waste management problems. This shift in emphasis was largely brought about by site selection activities for geologic repositories for disposal of high-level radioactive wastes. Model development during the 1970's and well into the 1980's focused primarily on saturated ground-water flow because geologic repositories in salt, basalt, granite, shale, and tuff were envisioned to be below the water table. Selection of the unsaturated zone at Yucca Mountain, Nevada, for potential disposal of waste began to shift model development toward unsaturated flow models. Under the US Department of Energy (DOE), the Civilian Radioactive Waste Management System Management and Operating Contractor (CRWMS M ampersand O) has the responsibility to review, evaluate, and document existing computer models; to conduct performance assessments; and to develop performance assessment models, where necessary. This document describes the CRWMS M ampersand O approach to model review and evaluation (Chapter 2), and the requirements for unsaturated flow models which are the bases for selection from among the current models (Chapter 3). Chapter 4 identifies existing models, and their characteristics. Through a detailed examination of characteristics, Chapter 5 presents the selection of models for testing. Chapter 6 discusses the testing and verification of selected models. Chapters 7 and 8 give conclusions and make recommendations, respectively. Chapter 9 records the major references for each of the models reviewed. Appendix A, a collection of technical reviews for each model, contains a more complete list of references. Finally, Appendix B characterizes the problems used for model testing
Graphical tools for model selection in generalized linear models.
Murray, K; Heritier, S; Müller, S
2013-11-10
Model selection techniques have existed for many years; however, to date, simple, clear and effective methods of visualising the model building process are sparse. This article describes graphical methods that assist in the selection of models and comparison of many different selection criteria. Specifically, we describe for logistic regression, how to visualize measures of description loss and of model complexity to facilitate the model selection dilemma. We advocate the use of the bootstrap to assess the stability of selected models and to enhance our graphical tools. We demonstrate which variables are important using variable inclusion plots and show that these can be invaluable plots for the model building process. We show with two case studies how these proposed tools are useful to learn more about important variables in the data and how these tools can assist the understanding of the model building process. Copyright © 2013 John Wiley & Sons, Ltd.
Accelerating Information Retrieval from Profile Hidden Markov Model Databases.
Tamimi, Ahmad; Ashhab, Yaqoub; Tamimi, Hashem
2016-01-01
Profile Hidden Markov Model (Profile-HMM) is an efficient statistical approach to represent protein families. Currently, several databases maintain valuable protein sequence information as profile-HMMs. There is an increasing interest to improve the efficiency of searching Profile-HMM databases to detect sequence-profile or profile-profile homology. However, most efforts to enhance searching efficiency have been focusing on improving the alignment algorithms. Although the performance of these algorithms is fairly acceptable, the growing size of these databases, as well as the increasing demand for using batch query searching approach, are strong motivations that call for further enhancement of information retrieval from profile-HMM databases. This work presents a heuristic method to accelerate the current profile-HMM homology searching approaches. The method works by cluster-based remodeling of the database to reduce the search space, rather than focusing on the alignment algorithms. Using different clustering techniques, 4284 TIGRFAMs profiles were clustered based on their similarities. A representative for each cluster was assigned. To enhance sensitivity, we proposed an extended step that allows overlapping among clusters. A validation benchmark of 6000 randomly selected protein sequences was used to query the clustered profiles. To evaluate the efficiency of our approach, speed and recall values were measured and compared with the sequential search approach. Using hierarchical, k-means, and connected component clustering techniques followed by the extended overlapping step, we obtained an average reduction in time of 41%, and an average recall of 96%. Our results demonstrate that representation of profile-HMMs using a clustering-based approach can significantly accelerate data retrieval from profile-HMM databases.
Accelerating Information Retrieval from Profile Hidden Markov Model Databases.
Directory of Open Access Journals (Sweden)
Ahmad Tamimi
Full Text Available Profile Hidden Markov Model (Profile-HMM is an efficient statistical approach to represent protein families. Currently, several databases maintain valuable protein sequence information as profile-HMMs. There is an increasing interest to improve the efficiency of searching Profile-HMM databases to detect sequence-profile or profile-profile homology. However, most efforts to enhance searching efficiency have been focusing on improving the alignment algorithms. Although the performance of these algorithms is fairly acceptable, the growing size of these databases, as well as the increasing demand for using batch query searching approach, are strong motivations that call for further enhancement of information retrieval from profile-HMM databases. This work presents a heuristic method to accelerate the current profile-HMM homology searching approaches. The method works by cluster-based remodeling of the database to reduce the search space, rather than focusing on the alignment algorithms. Using different clustering techniques, 4284 TIGRFAMs profiles were clustered based on their similarities. A representative for each cluster was assigned. To enhance sensitivity, we proposed an extended step that allows overlapping among clusters. A validation benchmark of 6000 randomly selected protein sequences was used to query the clustered profiles. To evaluate the efficiency of our approach, speed and recall values were measured and compared with the sequential search approach. Using hierarchical, k-means, and connected component clustering techniques followed by the extended overlapping step, we obtained an average reduction in time of 41%, and an average recall of 96%. Our results demonstrate that representation of profile-HMMs using a clustering-based approach can significantly accelerate data retrieval from profile-HMM databases.
Koua, Dominique; Kuhn-Nentwig, Lucia
2017-08-08
Spider venoms are rich cocktails of bioactive peptides, proteins, and enzymes that are being intensively investigated over the years. In order to provide a better comprehension of that richness, we propose a three-level family classification system for spider venom components. This classification is supported by an exhaustive set of 219 new profile hidden Markov models (HMMs) able to attribute a given peptide to its precise peptide type, family, and group. The proposed classification has the advantages of being totally independent from variable spider taxonomic names and can easily evolve. In addition to the new classifiers, we introduce and demonstrate the efficiency of hmmcompete , a new standalone tool that monitors HMM-based family classification and, after post-processing the result, reports the best classifier when multiple models produce significant scores towards given peptide queries. The combined used of hmmcompete and the new spider venom component-specific classifiers demonstrated 96% sensitivity to properly classify all known spider toxins from the UniProtKB database. These tools are timely regarding the important classification needs caused by the increasing number of peptides and proteins generated by transcriptomic projects.
HMM-based lexicon-driven and lexicon-free word recognition for online handwritten Indic scripts.
Bharath, A; Madhvanath, Sriganesh
2012-04-01
Research for recognizing online handwritten words in Indic scripts is at its early stages when compared to Latin and Oriental scripts. In this paper, we address this problem specifically for two major Indic scripts--Devanagari and Tamil. In contrast to previous approaches, the techniques we propose are largely data driven and script independent. We propose two different techniques for word recognition based on Hidden Markov Models (HMM): lexicon driven and lexicon free. The lexicon-driven technique models each word in the lexicon as a sequence of symbol HMMs according to a standard symbol writing order derived from the phonetic representation. The lexicon-free technique uses a novel Bag-of-Symbols representation of the handwritten word that is independent of symbol order and allows rapid pruning of the lexicon. On handwritten Devanagari word samples featuring both standard and nonstandard symbol writing orders, a combination of lexicon-driven and lexicon-free recognizers significantly outperforms either of them used in isolation. In contrast, most Tamil word samples feature the standard symbol order, and the lexicon-driven recognizer outperforms the lexicon free one as well as their combination. The best recognition accuracies obtained for 20,000 word lexicons are 87.13 percent for Devanagari when the two recognizers are combined, and 91.8 percent for Tamil using the lexicon-driven technique.
Genetic search feature selection for affective modeling
DEFF Research Database (Denmark)
Martínez, Héctor P.; Yannakakis, Georgios N.
2010-01-01
Automatic feature selection is a critical step towards the generation of successful computational models of affect. This paper presents a genetic search-based feature selection method which is developed as a global-search algorithm for improving the accuracy of the affective models built....... The method is tested and compared against sequential forward feature selection and random search in a dataset derived from a game survey experiment which contains bimodal input features (physiological and gameplay) and expressed pairwise preferences of affect. Results suggest that the proposed method...
Sand, Andreas; Kristiansen, Martin; Pedersen, Christian N S; Mailund, Thomas
2013-11-22
Hidden Markov models are widely used for genome analysis as they combine ease of modelling with efficient analysis algorithms. Calculating the likelihood of a model using the forward algorithm has worst case time complexity linear in the length of the sequence and quadratic in the number of states in the model. For genome analysis, however, the length runs to millions or billions of observations, and when maximising the likelihood hundreds of evaluations are often needed. A time efficient forward algorithm is therefore a key ingredient in an efficient hidden Markov model library. We have built a software library for efficiently computing the likelihood of a hidden Markov model. The library exploits commonly occurring substrings in the input to reuse computations in the forward algorithm. In a pre-processing step our library identifies common substrings and builds a structure over the computations in the forward algorithm which can be reused. This analysis can be saved between uses of the library and is independent of concrete hidden Markov models so one preprocessing can be used to run a number of different models.Using this library, we achieve up to 78 times shorter wall-clock time for realistic whole-genome analyses with a real and reasonably complex hidden Markov model. In one particular case the analysis was performed in less than 8 minutes compared to 9.6 hours for the previously fastest library. We have implemented the preprocessing procedure and forward algorithm as a C++ library, zipHMM, with Python bindings for use in scripts. The library is available at http://birc.au.dk/software/ziphmm/.
Selecting model complexity in learning problems
Energy Technology Data Exchange (ETDEWEB)
Buescher, K.L. [Los Alamos National Lab., NM (United States); Kumar, P.R. [Illinois Univ., Urbana, IL (United States). Coordinated Science Lab.
1993-10-01
To learn (or generalize) from noisy data, one must resist the temptation to pick a model for the underlying process that overfits the data. Many existing techniques solve this problem at the expense of requiring the evaluation of an absolute, a priori measure of each model`s complexity. We present a method that does not. Instead, it uses a natural, relative measure of each model`s complexity. This method first creates a pool of ``simple`` candidate models using part of the data and then selects from among these by using the rest of the data.
Model selection for Gaussian kernel PCA denoising
DEFF Research Database (Denmark)
Jørgensen, Kasper Winther; Hansen, Lars Kai
2012-01-01
We propose kernel Parallel Analysis (kPA) for automatic kernel scale and model order selection in Gaussian kernel PCA. Parallel Analysis [1] is based on a permutation test for covariance and has previously been applied for model order selection in linear PCA, we here augment the procedure to also...... tune the Gaussian kernel scale of radial basis function based kernel PCA.We evaluate kPA for denoising of simulated data and the US Postal data set of handwritten digits. We find that kPA outperforms other heuristics to choose the model order and kernel scale in terms of signal-to-noise ratio (SNR...
Melody Track Selection Using Discriminative Language Model
Wu, Xiao; Li, Ming; Suo, Hongbin; Yan, Yonghong
In this letter we focus on the task of selecting the melody track from a polyphonic MIDI file. Based on the intuition that music and language are similar in many aspects, we solve the selection problem by introducing an n-gram language model to learn the melody co-occurrence patterns in a statistical manner and determine the melodic degree of a given MIDI track. Furthermore, we propose the idea of using background model and posterior probability criteria to make modeling more discriminative. In the evaluation, the achieved 81.6% correct rate indicates the feasibility of our approach.
Model structure selection in convolutive mixtures
DEFF Research Database (Denmark)
Dyrholm, Mads; Makeig, Scott; Hansen, Lars Kai
2006-01-01
The CICAAR algorithm (convolutive independent component analysis with an auto-regressive inverse model) allows separation of white (i.i.d) source signals from convolutive mixtures. We introduce a source color model as a simple extension to the CICAAR which allows for a more parsimoneous...... representation in many practical mixtures. The new filter-CICAAR allows Bayesian model selection and can help answer questions like: 'Are we actually dealing with a convolutive mixture?'. We try to answer this question for EEG data....
On spatial mutation-selection models
Energy Technology Data Exchange (ETDEWEB)
Kondratiev, Yuri, E-mail: kondrat@math.uni-bielefeld.de [Fakultät für Mathematik, Universität Bielefeld, Postfach 100131, 33501 Bielefeld (Germany); Kutoviy, Oleksandr, E-mail: kutoviy@math.uni-bielefeld.de, E-mail: kutovyi@mit.edu [Fakultät für Mathematik, Universität Bielefeld, Postfach 100131, 33501 Bielefeld (Germany); Department of Mathematics, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139 (United States); Minlos, Robert, E-mail: minl@iitp.ru; Pirogov, Sergey, E-mail: pirogov@proc.ru [IITP, RAS, Bolshoi Karetnyi 19, Moscow (Russian Federation)
2013-11-15
We discuss the selection procedure in the framework of mutation models. We study the regulation for stochastically developing systems based on a transformation of the initial Markov process which includes a cost functional. The transformation of initial Markov process by cost functional has an analytic realization in terms of a Kimura-Maruyama type equation for the time evolution of states or in terms of the corresponding Feynman-Kac formula on the path space. The state evolution of the system including the limiting behavior is studied for two types of mutation-selection models.
Sparse model selection via integral terms
Schaeffer, Hayden; McCalla, Scott G.
2017-08-01
Model selection and parameter estimation are important for the effective integration of experimental data, scientific theory, and precise simulations. In this work, we develop a learning approach for the selection and identification of a dynamical system directly from noisy data. The learning is performed by extracting a small subset of important features from an overdetermined set of possible features using a nonconvex sparse regression model. The sparse regression model is constructed to fit the noisy data to the trajectory of the dynamical system while using the smallest number of active terms. Computational experiments detail the model's stability, robustness to noise, and recovery accuracy. Examples include nonlinear equations, population dynamics, chaotic systems, and fast-slow systems.
Adverse selection model regarding tobacco consumption
Directory of Open Access Journals (Sweden)
Dumitru MARIN
2006-01-01
Full Text Available The impact of introducing a tax on tobacco consumption can be studied trough an adverse selection model. The objective of the model presented in the following is to characterize the optimal contractual relationship between the governmental authorities and the two type employees: smokers and non-smokers, taking into account that the consumers’ decision to smoke or not represents an element of risk and uncertainty. Two scenarios are run using the General Algebraic Modeling Systems software: one without taxes set on tobacco consumption and another one with taxes set on tobacco consumption, based on an adverse selection model described previously. The results of the two scenarios are compared in the end of the paper: the wage earnings levels and the social welfare in case of a smoking agent and in case of a non-smoking agent.
Modeling and Selection of Software Service Variants
Wittern, John Erik
2015-01-01
Providers and consumers have to deal with variants, meaning alternative instances of a service?s design, implementation, deployment, or operation, when developing or delivering software services. This work presents service feature modeling to deal with associated challenges, comprising a language to represent software service variants and a set of methods for modeling and subsequent variant selection. This work?s evaluation includes a POC implementation and two real-life use cases.
Model Selection in Data Analysis Competitions
DEFF Research Database (Denmark)
Wind, David Kofoed; Winther, Ole
2014-01-01
The use of data analysis competitions for selecting the most appropriate model for a problem is a recent innovation in the field of predictive machine learning. Two of the most well-known examples of this trend was the Netflix Competition and recently the competitions hosted on the online platfor...
Efficiently adapting graphical models for selectivity estimation
DEFF Research Database (Denmark)
Tzoumas, Kostas; Deshpande, Amol; Jensen, Christian S.
2013-01-01
in estimation accuracy. We show how to efficiently construct such a graphical model from the database using only two-way join queries, and we show how to perform selectivity estimation in a highly efficient manner. We integrate our algorithms into the PostgreSQL DBMS. Experimental results indicate...
Model selection criterion in survival analysis
Karabey, Uǧur; Tutkun, Nihal Ata
2017-07-01
Survival analysis deals with time until occurrence of an event of interest such as death, recurrence of an illness, the failure of an equipment or divorce. There are various survival models with semi-parametric or parametric approaches used in medical, natural or social sciences. The decision on the most appropriate model for the data is an important point of the analysis. In literature Akaike information criteria or Bayesian information criteria are used to select among nested models. In this study,the behavior of these information criterion is discussed for a real data set.
On Using Selection Procedures with Binomial Models.
1983-10-01
eds.), Shinko Tsusho Co. Ltd., Tokyo, Japan , pp. 501-533. Gupta, S. S. and Sobel, M. (1960). Selecting a subset containing the best of several...IA_____3_6r__I____ *TITLE food A$ieweI L TYPE of 09PORT 6 PERIOD COVERED ON USING SELECTION PROCEDURES WITH BINOMIAL MODELS Technical 6. PeSPRFeauS1 ONG. REPORT...ontoedis stoc toeSI. to Ei.,..,t&* toemR.,. 14. SUPPOLEMENTARY MOCTES 19. Rey WORDS (Coatiou. 40 ow.oa* edo if Necesary and #do""&a by block number
Aerosol model selection and uncertainty modelling by adaptive MCMC technique
Directory of Open Access Journals (Sweden)
M. Laine
2008-12-01
Full Text Available We present a new technique for model selection problem in atmospheric remote sensing. The technique is based on Monte Carlo sampling and it allows model selection, calculation of model posterior probabilities and model averaging in Bayesian way.
The algorithm developed here is called Adaptive Automatic Reversible Jump Markov chain Monte Carlo method (AARJ. It uses Markov chain Monte Carlo (MCMC technique and its extension called Reversible Jump MCMC. Both of these techniques have been used extensively in statistical parameter estimation problems in wide area of applications since late 1990's. The novel feature in our algorithm is the fact that it is fully automatic and easy to use.
We show how the AARJ algorithm can be implemented and used for model selection and averaging, and to directly incorporate the model uncertainty. We demonstrate the technique by applying it to the statistical inversion problem of gas profile retrieval of GOMOS instrument on board the ENVISAT satellite. Four simple models are used simultaneously to describe the dependence of the aerosol cross-sections on wavelength. During the AARJ estimation all the models are used and we obtain a probability distribution characterizing how probable each model is. By using model averaging, the uncertainty related to selecting the aerosol model can be taken into account in assessing the uncertainty of the estimates.
Review and selection of unsaturated flow models
Energy Technology Data Exchange (ETDEWEB)
NONE
1993-09-10
Under the US Department of Energy (DOE), the Civilian Radioactive Waste Management System Management and Operating Contractor (CRWMS M&O) has the responsibility to review, evaluate, and document existing computer ground-water flow models; to conduct performance assessments; and to develop performance assessment models, where necessary. In the area of scientific modeling, the M&O CRWMS has the following responsibilities: To provide overall management and integration of modeling activities. To provide a framework for focusing modeling and model development. To identify areas that require increased or decreased emphasis. To ensure that the tools necessary to conduct performance assessment are available. These responsibilities are being initiated through a three-step process. It consists of a thorough review of existing models, testing of models which best fit the established requirements, and making recommendations for future development that should be conducted. Future model enhancement will then focus on the models selected during this activity. Furthermore, in order to manage future model development, particularly in those areas requiring substantial enhancement, the three-step process will be updated and reported periodically in the future.
Post-model selection inference and model averaging
Directory of Open Access Journals (Sweden)
Georges Nguefack-Tsague
2011-07-01
Full Text Available Although model selection is routinely used in practice nowadays, little is known about its precise effects on any subsequent inference that is carried out. The same goes for the effects induced by the closely related technique of model averaging. This paper is concerned with the use of the same data first to select a model and then to carry out inference, in particular point estimation and point prediction. The properties of the resulting estimator, called a post-model-selection estimator (PMSE, are hard to derive. Using selection criteria such as hypothesis testing, AIC, BIC, HQ and Cp, we illustrate that, in terms of risk function, no single PMSE dominates the others. The same conclusion holds more generally for any penalised likelihood information criterion. We also compare various model averaging schemes and show that no single one dominates the others in terms of risk function. Since PMSEs can be regarded as a special case of model averaging, with 0-1 random-weights, we propose a connection between the two theories, in the frequentist approach, by taking account of the selection procedure when performing model averaging. We illustrate the point by simulating a simple linear regression model.
Model structure selection in convolutive mixtures
DEFF Research Database (Denmark)
Dyrholm, Mads; Makeig, S.; Hansen, Lars Kai
2006-01-01
The CICAAR algorithm (convolutive independent component analysis with an auto-regressive inverse model) allows separation of white (i.i.d) source signals from convolutive mixtures. We introduce a source color model as a simple extension to the CICAAR which allows for a more parsimonious represent......The CICAAR algorithm (convolutive independent component analysis with an auto-regressive inverse model) allows separation of white (i.i.d) source signals from convolutive mixtures. We introduce a source color model as a simple extension to the CICAAR which allows for a more parsimonious...... representation in many practical mixtures. The new filter-CICAAR allows Bayesian model selection and can help answer questions like: ’Are we actually dealing with a convolutive mixture?’. We try to answer this question for EEG data....
Skewed factor models using selection mechanisms
Kim, Hyoung-Moon
2015-12-21
Traditional factor models explicitly or implicitly assume that the factors follow a multivariate normal distribution; that is, only moments up to order two are involved. However, it may happen in real data problems that the first two moments cannot explain the factors. Based on this motivation, here we devise three new skewed factor models, the skew-normal, the skew-tt, and the generalized skew-normal factor models depending on a selection mechanism on the factors. The ECME algorithms are adopted to estimate related parameters for statistical inference. Monte Carlo simulations validate our new models and we demonstrate the need for skewed factor models using the classic open/closed book exam scores dataset.
Chemical identification using Bayesian model selection
Energy Technology Data Exchange (ETDEWEB)
Burr, Tom; Fry, H. A. (Herbert A.); McVey, B. D. (Brian D.); Sander, E. (Eric)
2002-01-01
Remote detection and identification of chemicals in a scene is a challenging problem. We introduce an approach that uses some of the image's pixels to establish the background characteristics while other pixels represent the target for which we seek to identify all chemical species present. This leads to a generalized least squares problem in which we focus on 'subset selection' to identify the chemicals thought to be present. Bayesian model selection allows us to approximate the posterior probability that each chemical in the library is present by adding the posterior probabilities of all the subsets which include the chemical. We present results using realistic simulated data for the case with 1 to 5 chemicals present in each target and compare performance to a hybrid of forward and backward stepwise selection procedure using the F statistic.
Expatriates Selection: An Essay of Model Analysis
Directory of Open Access Journals (Sweden)
Rui Bártolo-Ribeiro
2015-03-01
Full Text Available The business expansion to other geographical areas with different cultures from which organizations were created and developed leads to the expatriation of employees to these destinations. Recruitment and selection procedures of expatriates do not always have the intended success leading to an early return of these professionals with the consequent organizational disorders. In this study, several articles published in the last five years were analyzed in order to identify the most frequently mentioned dimensions in the selection of expatriates in terms of success and failure. The characteristics in the selection process that may increase prediction of adaptation of expatriates to new cultural contexts of the some organization were studied according to the KSAOs model. Few references were found concerning Knowledge, Skills and Abilities dimensions in the analyzed papers. There was a strong predominance on the evaluation of Other Characteristics, and was given more importance to dispositional factors than situational factors for promoting the integration of the expatriates.
HPeak: an HMM-based algorithm for defining read-enriched regions in ChIP-Seq data
Directory of Open Access Journals (Sweden)
Maher Christopher A
2010-07-01
Full Text Available Abstract Background Protein-DNA interaction constitutes a basic mechanism for the genetic regulation of target gene expression. Deciphering this mechanism has been a daunting task due to the difficulty in characterizing protein-bound DNA on a large scale. A powerful technique has recently emerged that couples chromatin immunoprecipitation (ChIP with next-generation sequencing, (ChIP-Seq. This technique provides a direct survey of the cistrom of transcription factors and other chromatin-associated proteins. In order to realize the full potential of this technique, increasingly sophisticated statistical algorithms have been developed to analyze the massive amount of data generated by this method. Results Here we introduce HPeak, a Hidden Markov model (HMM-based Peak-finding algorithm for analyzing ChIP-Seq data to identify protein-interacting genomic regions. In contrast to the majority of available ChIP-Seq analysis software packages, HPeak is a model-based approach allowing for rigorous statistical inference. This approach enables HPeak to accurately infer genomic regions enriched with sequence reads by assuming realistic probability distributions, in conjunction with a novel weighting scheme on the sequencing read coverage. Conclusions Using biologically relevant data collections, we found that HPeak showed a higher prevalence of the expected transcription factor binding motifs in ChIP-enriched sequences relative to the control sequences when compared to other currently available ChIP-Seq analysis approaches. Additionally, in comparison to the ChIP-chip assay, ChIP-Seq provides higher resolution along with improved sensitivity and specificity of binding site detection. Additional file and the HPeak program are freely available at http://www.sph.umich.edu/csg/qin/HPeak.
Reserve selection using nonlinear species distribution models.
Moilanen, Atte
2005-06-01
Reserve design is concerned with optimal selection of sites for new conservation areas. Spatial reserve design explicitly considers the spatial pattern of the proposed reserve network and the effects of that pattern on reserve cost and/or ability to maintain species there. The vast majority of reserve selection formulations have assumed a linear problem structure, which effectively means that the biological value of a potential reserve site does not depend on the pattern of selected cells. However, spatial population dynamics and autocorrelation cause the biological values of neighboring sites to be interdependent. Habitat degradation may have indirect negative effects on biodiversity in areas neighboring the degraded site as a result of, for example, negative edge effects or lower permeability for animal movement. In this study, I present a formulation and a spatial optimization algorithm for nonlinear reserve selection problems in grid-based landscapes that accounts for interdependent site values. The method is demonstrated using habitat maps and nonlinear habitat models for threatened birds in the Netherlands, and it is shown that near-optimal solutions are found for regions consisting of up to hundreds of thousands grid cells, a landscape size much larger than those commonly attempted even with linear reserve selection formulations.
Name segmentation using hidden Markov models and its application in record linkage
Directory of Open Access Journals (Sweden)
Rita de Cassia Braga Gonçalves
2014-10-01
Full Text Available This study aimed to evaluate the use of hidden Markov models (HMM for the segmentation of person names and its influence on record linkage. A HMM was applied to the segmentation of patient’s and mother’s names in the databases of the Mortality Information System (SIM, Information Subsystem for High Complexity Procedures (APAC, and Hospital Information System (AIH. A sample of 200 patients from each database was segmented via HMM, and the results were compared to those from segmentation by the authors. The APAC-SIM and APAC-AIH databases were linked using three different segmentation strategies, one of which used HMM. Conformity of segmentation via HMM varied from 90.5% to 92.5%. The different segmentation strategies yielded similar results in the record linkage process. This study suggests that segmentation of Brazilian names via HMM is no more effective than traditional segmentation approaches in the linkage process.
Name segmentation using hidden Markov models and its application in record linkage.
Gonçalves, Rita de Cassia Braga; Freire, Sergio Miranda
2014-10-01
This study aimed to evaluate the use of hidden Markov models (HMM) for the segmentation of person names and its influence on record linkage. A HMM was applied to the segmentation of patient's and mother's names in the databases of the Mortality Information System (SIM), Information Subsystem for High Complexity Procedures (APAC), and Hospital Information System (AIH). A sample of 200 patients from each database was segmented via HMM, and the results were compared to those from segmentation by the authors. The APAC-SIM and APAC-AIH databases were linked using three different segmentation strategies, one of which used HMM. Conformity of segmentation via HMM varied from 90.5% to 92.5%. The different segmentation strategies yielded similar results in the record linkage process. This study suggests that segmentation of Brazilian names via HMM is no more effective than traditional segmentation approaches in the linkage process.
Behavioral optimization models for multicriteria portfolio selection
Directory of Open Access Journals (Sweden)
Mehlawat Mukesh Kumar
2013-01-01
Full Text Available In this paper, behavioral construct of suitability is used to develop a multicriteria decision making framework for portfolio selection. To achieve this purpose, we rely on multiple methodologies. Analytical hierarchy process technique is used to model the suitability considerations with a view to obtaining the suitability performance score in respect of each asset. A fuzzy multiple criteria decision making method is used to obtain the financial quality score of each asset based upon investor's rating on the financial criteria. Two optimization models are developed for optimal asset allocation considering simultaneously financial and suitability criteria. An empirical study is conducted on randomly selected assets from National Stock Exchange, Mumbai, India to demonstrate the effectiveness of the proposed methodology.
Multi-dimensional model order selection
Directory of Open Access Journals (Sweden)
Roemer Florian
2011-01-01
Full Text Available Abstract Multi-dimensional model order selection (MOS techniques achieve an improved accuracy, reliability, and robustness, since they consider all dimensions jointly during the estimation of parameters. Additionally, from fundamental identifiability results of multi-dimensional decompositions, it is known that the number of main components can be larger when compared to matrix-based decompositions. In this article, we show how to use tensor calculus to extend matrix-based MOS schemes and we also present our proposed multi-dimensional model order selection scheme based on the closed-form PARAFAC algorithm, which is only applicable to multi-dimensional data. In general, as shown by means of simulations, the Probability of correct Detection (PoD of our proposed multi-dimensional MOS schemes is much better than the PoD of matrix-based schemes.
A simple parametric model selection test
Susanne M. Schennach; Daniel Wilhelm
2014-01-01
We propose a simple model selection test for choosing among two parametric likelihoods which can be applied in the most general setting without any assumptions on the relation between the candidate models and the true distribution. That is, both, one or neither is allowed to be correctly speci fied or misspeci fied, they may be nested, non-nested, strictly non-nested or overlapping. Unlike in previous testing approaches, no pre-testing is needed, since in each case, the same test statistic to...
Robust inference in sample selection models
Zhelonkin, Mikhail
2015-11-20
The problem of non-random sample selectivity often occurs in practice in many fields. The classical estimators introduced by Heckman are the backbone of the standard statistical analysis of these models. However, these estimators are very sensitive to small deviations from the distributional assumptions which are often not satisfied in practice. We develop a general framework to study the robustness properties of estimators and tests in sample selection models. We derive the influence function and the change-of-variance function of Heckman\\'s two-stage estimator, and we demonstrate the non-robustness of this estimator and its estimated variance to small deviations from the model assumed. We propose a procedure for robustifying the estimator, prove its asymptotic normality and give its asymptotic variance. Both cases with and without an exclusion restriction are covered. This allows us to construct a simple robust alternative to the sample selection bias test. We illustrate the use of our new methodology in an analysis of ambulatory expenditures and we compare the performance of the classical and robust methods in a Monte Carlo simulation study.
Novel metrics for growth model selection.
Grigsby, Matthew R; Di, Junrui; Leroux, Andrew; Zipunnikov, Vadim; Xiao, Luo; Crainiceanu, Ciprian; Checkley, William
2018-01-01
Literature surrounding the statistical modeling of childhood growth data involves a diverse set of potential models from which investigators can choose. However, the lack of a comprehensive framework for comparing non-nested models leads to difficulty in assessing model performance. This paper proposes a framework for comparing non-nested growth models using novel metrics of predictive accuracy based on modifications of the mean squared error criteria. Three metrics were created: normalized, age-adjusted, and weighted mean squared error (MSE). Predictive performance metrics were used to compare linear mixed effects models and functional regression models. Prediction accuracy was assessed by partitioning the observed data into training and test datasets. This partitioning was constructed to assess prediction accuracy for backward (i.e., early growth), forward (i.e., late growth), in-range, and on new-individuals. Analyses were done with height measurements from 215 Peruvian children with data spanning from near birth to 2 years of age. Functional models outperformed linear mixed effects models in all scenarios tested. In particular, prediction errors for functional concurrent regression (FCR) and functional principal component analysis models were approximately 6% lower when compared to linear mixed effects models. When we weighted subject-specific MSEs according to subject-specific growth rates during infancy, we found that FCR was the best performer in all scenarios. With this novel approach, we can quantitatively compare non-nested models and weight subgroups of interest to select the best performing growth model for a particular application or problem at hand.
Directory of Open Access Journals (Sweden)
Juri Taborri
2014-09-01
Full Text Available In this work, we decided to apply a hierarchical weighted decision, proposed and used in other research fields, for the recognition of gait phases. The developed and validated novel distributed classifier is based on hierarchical weighted decision from outputs of scalar Hidden Markov Models (HMM applied to angular velocities of foot, shank, and thigh. The angular velocities of ten healthy subjects were acquired via three uni-axial gyroscopes embedded in inertial measurement units (IMUs during one walking task, repeated three times, on a treadmill. After validating the novel distributed classifier and scalar and vectorial classifiers-already proposed in the literature, with a cross-validation, classifiers were compared for sensitivity, specificity, and computational load for all combinations of the three targeted anatomical segments. Moreover, the performance of the novel distributed classifier in the estimation of gait variability in terms of mean time and coefficient of variation was evaluated. The highest values of specificity and sensitivity (>0.98 for the three classifiers examined here were obtained when the angular velocity of the foot was processed. Distributed and vectorial classifiers reached acceptable values (>0.95 when the angular velocity of shank and thigh were analyzed. Distributed and scalar classifiers showed values of computational load about 100 times lower than the one obtained with the vectorial classifier. In addition, distributed classifiers showed an excellent reliability for the evaluation of mean time and a good/excellent reliability for the coefficient of variation. In conclusion, due to the better performance and the small value of computational load, the here proposed novel distributed classifier can be implemented in the real-time application of gait phases recognition, such as to evaluate gait variability in patients or to control active orthoses for the recovery of mobility of lower limb joints.
A new machine condition monitoring method based on likelihood change of a stochastic model
Hwang, Kyu Hwan; Lee, Jong Min; Hwang, Yoha
2013-12-01
In industry, a machine condition monitoring system has become more important with ever-increasing requirements on productivity and cost saving. Although researches have been very active, many currently available intelligent monitoring methods have common drawbacks, which are the requirement of defect model for every interested defect type and inaccurate diagnostic performance. To overcome those drawbacks, authors propose a new machine condition monitoring method based on likelihood change of a stochastic model using only normal operation data. Hidden Markov model (HMM) has been selected as a stochastic model based on its accurate and robust diagnostic performance. By observing the likelihood change of a pre-trained normal HMM on incoming data in unknown condition, defect can be precisely detected from sudden drop of likelihood value. Therefore, though the types of defect cannot be identified, defects can be precisely detected with only normal model. Defect models can also be used when defect data are available. And in this case, not only the precise detection of defect but also the correct identification of defect type is possible. In this paper, the proposed monitoring method based on likelihood change of normal continuous HMM have been successfully applied to monitoring of the machine condition and weld condition, proving its great potential with accurate and robust diagnostic performance results.
Pair Hidden Markov Model for Named Entity Matching
Nabende, P.; Tiedemann, J.; Nerbonne, J.; Sobh, T.
2010-01-01
This paper introduces a pair-Hidden Markov Model (pair-HMM) for the task of evaluating the similarity between bilingual named entities. The pair-HMM is adapted from Mackay and Kondrak [1] who used it on the task of cognate identification and was later adapted by Wieling et al. [5] for Dutch dialect
Model selection and comparison for independents sinusoids
DEFF Research Database (Denmark)
Nielsen, Jesper Kjær; Christensen, Mads Græsbøll; Jensen, Søren Holdt
2014-01-01
In the signal processing literature, many methods have been proposed for estimating the number of sinusoidal basis functions from a noisy data set. The most popular method is the asymptotic MAP criterion, which is sometimes also referred to as the BIC. In this paper, we extend and improve this me....... Through simulations, we demonstrate that the lp-BIC outperforms the asymptotic MAP criterion and other state of the art methods in terms of model selection, de-noising and prediction performance. The simulation code is available online.......In the signal processing literature, many methods have been proposed for estimating the number of sinusoidal basis functions from a noisy data set. The most popular method is the asymptotic MAP criterion, which is sometimes also referred to as the BIC. In this paper, we extend and improve...... this method by considering the problem in a full Bayesian framework instead of the approximate formulation, on which the asymptotic MAP criterion is based. This leads to a new model selection and comparison method, the lp-BIC, whose computational complexity is of the same order as the asymptotic MAP criterion...
Dang, Shilpa; Chaudhury, Santanu; Lall, Brejesh; Roy, Prasun Kumar
2017-02-15
Effective connectivity (EC) analysis of neuronal groups using fMRI delivers insights about functional-integration. However, fMRI signal has low-temporal resolution due to down-sampling and indirectly measures underlying neuronal activity. The aim is to address above issues for more reliable EC estimates. This paper proposes use of autoregressive hidden Markov model with missing data (AR-HMM-md) in dynamically multi-linked (DML) framework for learning EC using multiple fMRI time series. In our recent work (Dang et al., 2016), we have shown how AR-HMM-md for modelling single fMRI time series outperforms the existing methods. AR-HMM-md models unobserved neuronal activity and lost data over time as variables and estimates their values by joint optimization given fMRI observation sequence. The effectiveness in learning EC is shown using simulated experiments. Also the effects of sampling and noise are studied on EC. Moreover, classification-experiments are performed for Attention-Deficit/Hyperactivity Disorder subjects and age-matched controls for performance evaluation of real data. Using Bayesian model selection, we see that the proposed model converged to higher log-likelihood and demonstrated that group-classification can be performed with higher cross-validation accuracy of above 94% using distinctive network EC which characterizes patients vs. The full data EC obtained from DML-AR-HMM-md is more consistent with previous literature than the classical multivariate Granger causality method. The proposed architecture leads to reliable estimates of EC than the existing latent models. This framework overcomes the disadvantage of low-temporal resolution and improves cross-validation accuracy significantly due to presence of missing data variables and autoregressive process. Copyright © 2016 Elsevier B.V. All rights reserved.
Bayesian Model Selection in Geophysics: The evidence
Vrugt, J. A.
2016-12-01
Bayesian inference has found widespread application and use in science and engineering to reconcile Earth system models with data, including prediction in space (interpolation), prediction in time (forecasting), assimilation of observations and deterministic/stochastic model output, and inference of the model parameters. Per Bayes theorem, the posterior probability, , P(H|D), of a hypothesis, H, given the data D, is equivalent to the product of its prior probability, P(H), and likelihood, L(H|D), divided by a normalization constant, P(D). In geophysics, the hypothesis, H, often constitutes a description (parameterization) of the subsurface for some entity of interest (e.g. porosity, moisture content). The normalization constant, P(D), is not required for inference of the subsurface structure, yet of great value for model selection. Unfortunately, it is not particularly easy to estimate P(D) in practice. Here, I will introduce the various building blocks of a general purpose method which provides robust and unbiased estimates of the evidence, P(D). This method uses multi-dimensional numerical integration of the posterior (parameter) distribution. I will then illustrate this new estimator by application to three competing subsurface models (hypothesis) using GPR travel time data from the South Oyster Bacterial Transport Site, in Virginia, USA. The three subsurface models differ in their treatment of the porosity distribution and use (a) horizontal layering with fixed layer thicknesses, (b) vertical layering with fixed layer thicknesses and (c) a multi-Gaussian field. The results of the new estimator are compared against the brute force Monte Carlo method, and the Laplace-Metropolis method.
High-dimensional model estimation and model selection
CERN. Geneva
2015-01-01
I will review concepts and algorithms from high-dimensional statistics for linear model estimation and model selection. I will particularly focus on the so-called p>>n setting where the number of variables p is much larger than the number of samples n. I will focus mostly on regularized statistical estimators that produce sparse models. Important examples include the LASSO and its matrix extension, the Graphical LASSO, and more recent non-convex methods such as the TREX. I will show the applicability of these estimators in a diverse range of scientific applications, such as sparse interaction graph recovery and high-dimensional classification and regression problems in genomics.
SVM-dependent pairwise HMM: an application to protein pairwise alignments.
Orlando, Gabriele; Raimondi, Daniele; Khan, Taushif; Lenaerts, Tom; Vranken, Wim F
2017-12-15
Methods able to provide reliable protein alignments are crucial for many bioinformatics applications. In the last years many different algorithms have been developed and various kinds of information, from sequence conservation to secondary structure, have been used to improve the alignment performances. This is especially relevant for proteins with highly divergent sequences. However, recent works suggest that different features may have different importance in diverse protein classes and it would be an advantage to have more customizable approaches, capable to deal with different alignment definitions. Here we present Rigapollo, a highly flexible pairwise alignment method based on a pairwise HMM-SVM that can use any type of information to build alignments. Rigapollo lets the user decide the optimal features to align their protein class of interest. It outperforms current state of the art methods on two well-known benchmark datasets when aligning highly divergent sequences. A Python implementation of the algorithm is available at http://ibsquare.be/rigapollo. wim.vranken@vub.be. Supplementary data are available at Bioinformatics online. © The Author (2017). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com
Selecting a model of supersymmetry breaking mediation
International Nuclear Information System (INIS)
AbdusSalam, S. S.; Allanach, B. C.; Dolan, M. J.; Feroz, F.; Hobson, M. P.
2009-01-01
We study the problem of selecting between different mechanisms of supersymmetry breaking in the minimal supersymmetric standard model using current data. We evaluate the Bayesian evidence of four supersymmetry breaking scenarios: mSUGRA, mGMSB, mAMSB, and moduli mediation. The results show a strong dependence on the dark matter assumption. Using the inferred cosmological relic density as an upper bound, minimal anomaly mediation is at least moderately favored over the CMSSM. Our fits also indicate that evidence for a positive sign of the μ parameter is moderate at best. We present constraints on the anomaly and gauge mediated parameter spaces and some previously unexplored aspects of the dark matter phenomenology of the moduli mediation scenario. We use sparticle searches, indirect observables and dark matter observables in the global fit and quantify robustness with respect to prior choice. We quantify how much information is contained within each constraint.
Psyche Mission: Scientific Models and Instrument Selection
Polanskey, C. A.; Elkins-Tanton, L. T.; Bell, J. F., III; Lawrence, D. J.; Marchi, S.; Park, R. S.; Russell, C. T.; Weiss, B. P.
2017-12-01
NASA has chosen to explore (16) Psyche with their 14th Discovery-class mission. Psyche is a 226-km diameter metallic asteroid hypothesized to be the exposed core of a planetesimal that was stripped of its rocky mantle by multiple hit and run collisions in the early solar system. The spacecraft launch is planned for 2022 with arrival at the asteroid in 2026 for 21 months of operations. The Psyche investigation has five primary scientific objectives: A. Determine whether Psyche is a core, or if it is unmelted material. B. Determine the relative ages of regions of Psyche's surface. C. Determine whether small metal bodies incorporate the same light elements as are expected in the Earth's high-pressure core. D. Determine whether Psyche was formed under conditions more oxidizing or more reducing than Earth's core. E. Characterize Psyche's topography. The mission's task was to select the appropriate instruments to meet these objectives. However, exploring a metal world, rather than one made of ice, rock, or gas, requires development of new scientific models for Psyche to support the selection of the appropriate instruments for the payload. If Psyche is indeed a planetary core, we expect that it should have a detectable magnetic field. However, the strength of the magnetic field can vary by orders of magnitude depending on the formational history of Psyche. The implications of both the extreme low-end and the high-end predictions impact the magnetometer and mission design. For the imaging experiment, what can the team expect for the morphology of a heavily impacted metal body? Efforts are underway to further investigate the differences in crater morphology between high velocity impacts into metal and rock to be prepared to interpret the images of Psyche when they are returned. Finally, elemental composition measurements at Psyche using nuclear spectroscopy encompass a new and unexplored phase space of gamma-ray and neutron measurements. We will present some end
Coding with partially hidden Markov models
DEFF Research Database (Denmark)
Forchhammer, Søren; Rissanen, J.
1995-01-01
Partially hidden Markov models (PHMM) are introduced. They are a variation of the hidden Markov models (HMM) combining the power of explicit conditioning on past observations and the power of using hidden states. (P)HMM may be combined with arithmetic coding for lossless data compression. A general....... The PHMM structure and the conditions of the convergence proof allows for application of the PHMM to image coding. Relations between the PHMM and hidden Markov models (HMM) are treated. Results of coding bi-level images with the PHMM coding scheme is given. The results indicate that the PHMM can adapt...
A new Russell model for selecting suppliers
Azadi, Majid; Shabani, Amir; Farzipoor Saen, Reza
2014-01-01
Recently, supply chain management (SCM) has been considered by many researchers. Supplier evaluation and selection plays a significant role in establishing an effective SCM. One of the techniques that can be used for selecting suppliers is data envelopment analysis (DEA). In some situations, to
Selective experimental review of the Standard Model
International Nuclear Information System (INIS)
Bloom, E.D.
1985-02-01
Before disussing experimental comparisons with the Standard Model, (S-M) it is probably wise to define more completely what is commonly meant by this popular term. This model is a gauge theory of SU(3)/sub f/ x SU(2)/sub L/ x U(1) with 18 parameters. The parameters are α/sub s/, α/sub qed/, theta/sub W/, M/sub W/ (M/sub Z/ = M/sub W//cos theta/sub W/, and thus is not an independent parameter), M/sub Higgs/; the lepton masses, M/sub e/, Mμ, M/sub r/; the quark masses, M/sub d/, M/sub s/, M/sub b/, and M/sub u/, M/sub c/, M/sub t/; and finally, the quark mixing angles, theta 1 , theta 2 , theta 3 , and the CP violating phase delta. The latter four parameters appear in the quark mixing matrix for the Kobayashi-Maskawa and Maiani forms. Clearly, the present S-M covers an enormous range of physics topics, and the author can only lightly cover a few such topics in this report. The measurement of R/sub hadron/ is fundamental as a test of the running coupling constant α/sub s/ in QCD. The author will discuss a selection of recent precision measurements of R/sub hadron/, as well as some other techniques for measuring α/sub s/. QCD also requires the self interaction of gluons. The search for the three gluon vertex may be practically realized in the clear identification of gluonic mesons. The author will present a limited review of recent progress in the attempt to untangle such mesons from the plethora q anti q states of the same quantum numbers which exist in the same mass range. The electroweak interactions provide some of the strongest evidence supporting the S-M that exists. Given the recent progress in this subfield, and particularly with the discovery of the W and Z bosons at CERN, many recent reviews obviate the need for further discussion in this report. In attempting to validate a theory, one frequently searches for new phenomena which would clearly invalidate it. 49 references, 28 figures
Huda, Shamsul; Yearwood, John; Togneri, Roberto
2014-10-01
The expectation maximization (EM) is the standard training algorithm for hidden Markov model (HMM). However, EM faces a local convergence problem in HMM estimation. This paper attempts to overcome this problem of EM and proposes hybrid metaheuristic approaches to EM for HMM. In our earlier research, a hybrid of a constraint-based evolutionary learning approach to EM (CEL-EM) improved HMM estimation. In this paper, we propose a hybrid simulated annealing stochastic version of EM (SASEM) that combines simulated annealing (SA) with EM. The novelty of our approach is that we develop a mathematical reformulation of HMM estimation by introducing a stochastic step between the EM steps and combine SA with EM to provide better control over the acceptance of stochastic and EM steps for better HMM estimation. We also extend our earlier work and propose a second hybrid which is a combination of an EA and the proposed SASEM, (EA-SASEM). The proposed EA-SASEM uses the best constraint-based EA strategies from CEL-EM and stochastic reformulation of HMM. The complementary properties of EA and SA and stochastic reformulation of HMM of SASEM provide EA-SASEM with sufficient potential to find better estimation for HMM. To the best of our knowledge, this type of hybridization and mathematical reformulation have not been explored in the context of EM and HMM training. The proposed approaches have been evaluated through comprehensive experiments to justify their effectiveness in signal modeling using the speech corpus: TIMIT. Experimental results show that proposed approaches obtain higher recognition accuracies than the EM algorithm and CEL-EM as well.
Thomas, D.L.; Johnson, D.; Griffith, B.
2006-01-01
Modeling the probability of use of land units characterized by discrete and continuous measures, we present a Bayesian random-effects model to assess resource selection. This model provides simultaneous estimation of both individual- and population-level selection. Deviance information criterion (DIC), a Bayesian alternative to AIC that is sample-size specific, is used for model selection. Aerial radiolocation data from 76 adult female caribou (Rangifer tarandus) and calf pairs during 1 year on an Arctic coastal plain calving ground were used to illustrate models and assess population-level selection of landscape attributes, as well as individual heterogeneity of selection. Landscape attributes included elevation, NDVI (a measure of forage greenness), and land cover-type classification. Results from the first of a 2-stage model-selection procedure indicated that there is substantial heterogeneity among cow-calf pairs with respect to selection of the landscape attributes. In the second stage, selection of models with heterogeneity included indicated that at the population-level, NDVI and land cover class were significant attributes for selection of different landscapes by pairs on the calving ground. Population-level selection coefficients indicate that the pairs generally select landscapes with higher levels of NDVI, but the relationship is quadratic. The highest rate of selection occurs at values of NDVI less than the maximum observed. Results for land cover-class selections coefficients indicate that wet sedge, moist sedge, herbaceous tussock tundra, and shrub tussock tundra are selected at approximately the same rate, while alpine and sparsely vegetated landscapes are selected at a lower rate. Furthermore, the variability in selection by individual caribou for moist sedge and sparsely vegetated landscapes is large relative to the variability in selection of other land cover types. The example analysis illustrates that, while sometimes computationally intense, a
HMM_Model-Checker pour la vérification probabiliste | Ferroum ...
African Journals Online (AJOL)
La vérification probabiliste des systèmes embarqués continue de compter de plus en plus d'adeptes dans la communauté de chercheurs. Étant donné un modèle probabiliste, une formule de la logique temporelle, décrivant une propriété du système et un algorithme d'exploration permettant de vérifier si cette dernière est ...
Enhancing Speech Recognition Using Improved Particle Swarm Optimization Based Hidden Markov Model
Directory of Open Access Journals (Sweden)
Lokesh Selvaraj
2014-01-01
Full Text Available Enhancing speech recognition is the primary intention of this work. In this paper a novel speech recognition method based on vector quantization and improved particle swarm optimization (IPSO is suggested. The suggested methodology contains four stages, namely, (i denoising, (ii feature mining (iii, vector quantization, and (iv IPSO based hidden Markov model (HMM technique (IP-HMM. At first, the speech signals are denoised using median filter. Next, characteristics such as peak, pitch spectrum, Mel frequency Cepstral coefficients (MFCC, mean, standard deviation, and minimum and maximum of the signal are extorted from the denoised signal. Following that, to accomplish the training process, the extracted characteristics are given to genetic algorithm based codebook generation in vector quantization. The initial populations are created by selecting random code vectors from the training set for the codebooks for the genetic algorithm process and IP-HMM helps in doing the recognition. At this point the creativeness will be done in terms of one of the genetic operation crossovers. The proposed speech recognition technique offers 97.14% accuracy.
Uncertainty associated with selected environmental transport models
International Nuclear Information System (INIS)
Little, C.A.; Miller, C.W.
1979-11-01
A description is given of the capabilities of several models to predict accurately either pollutant concentrations in environmental media or radiological dose to human organs. The models are discussed in three sections: aquatic or surface water transport models, atmospheric transport models, and terrestrial and aquatic food chain models. Using data published primarily by model users, model predictions are compared to observations. This procedure is infeasible for food chain models and, therefore, the uncertainty embodied in the models input parameters, rather than the model output, is estimated. Aquatic transport models are divided into one-dimensional, longitudinal-vertical, and longitudinal-horizontal models. Several conclusions were made about the ability of the Gaussian plume atmospheric dispersion model to predict accurately downwind air concentrations from releases under several sets of conditions. It is concluded that no validation study has been conducted to test the predictions of either aquatic or terrestrial food chain models. Using the aquatic pathway from water to fish to an adult for 137 Cs as an example, a 95% one-tailed confidence limit interval for the predicted exposure is calculated by examining the distributions of the input parameters. Such an interval is found to be 16 times the value of the median exposure. A similar one-tailed limit for the air-grass-cow-milk-thyroid for 131 I and infants was 5.6 times the median dose. Of the three model types discussed in this report,the aquatic transport models appear to do the best job of predicting observed concentrations. However, this conclusion is based on many fewer aquatic validation data than were availaable for atmospheric model validation
Score-based prediction of genomic islands in prokaryotic genomes using hidden Markov models
Directory of Open Access Journals (Sweden)
Surovcik Katharina
2006-03-01
Full Text Available Abstract Background Horizontal gene transfer (HGT is considered a strong evolutionary force shaping the content of microbial genomes in a substantial manner. It is the difference in speed enabling the rapid adaptation to changing environmental demands that distinguishes HGT from gene genesis, duplications or mutations. For a precise characterization, algorithms are needed that identify transfer events with high reliability. Frequently, the transferred pieces of DNA have a considerable length, comprise several genes and are called genomic islands (GIs or more specifically pathogenicity or symbiotic islands. Results We have implemented the program SIGI-HMM that predicts GIs and the putative donor of each individual alien gene. It is based on the analysis of codon usage (CU of each individual gene of a genome under study. CU of each gene is compared against a carefully selected set of CU tables representing microbial donors or highly expressed genes. Multiple tests are used to identify putatively alien genes, to predict putative donors and to mask putatively highly expressed genes. Thus, we determine the states and emission probabilities of an inhomogeneous hidden Markov model working on gene level. For the transition probabilities, we draw upon classical test theory with the intention of integrating a sensitivity controller in a consistent manner. SIGI-HMM was written in JAVA and is publicly available. It accepts as input any file created according to the EMBL-format. It generates output in the common GFF format readable for genome browsers. Benchmark tests showed that the output of SIGI-HMM is in agreement with known findings. Its predictions were both consistent with annotated GIs and with predictions generated by different methods. Conclusion SIGI-HMM is a sensitive tool for the identification of GIs in microbial genomes. It allows to interactively analyze genomes in detail and to generate or to test hypotheses about the origin of acquired
Quality Quandaries- Time Series Model Selection and Parsimony
DEFF Research Database (Denmark)
Bisgaard, Søren; Kulahci, Murat
2009-01-01
Some of the issues involved in selecting adequate models for time series data are discussed using an example concerning the number of users of an Internet server. The process of selecting an appropriate model is subjective and requires experience and judgment. The authors believe an important...... consideration in model selection should be parameter parsimony. They favor the use of parsimonious mixed ARMA models, noting that research has shown that a model building strategy that considers only autoregressive representations will lead to non-parsimonious models and to loss of forecasting accuracy....
Genetic Algorithms Principles Towards Hidden Markov Model
Directory of Open Access Journals (Sweden)
Nabil M. Hewahi
2011-10-01
Full Text Available In this paper we propose a general approach based on Genetic Algorithms (GAs to evolve Hidden Markov Models (HMM. The problem appears when experts assign probability values for HMM, they use only some limited inputs. The assigned probability values might not be accurate to serve in other cases related to the same domain. We introduce an approach based on GAs to find
out the suitable probability values for the HMM to be mostly correct in more cases than what have been used to assign the probability values.
Directory of Open Access Journals (Sweden)
Shahin Ismail
2005-01-01
Full Text Available Speaker identification systems perform well under the neutral talking condition; however, they suffer sharp degradation under the shouted talking condition. In this paper, the second-order hidden Markov models (HMM2s have been used to improve the recognition performance of isolated-word text-dependent speaker identification systems under the shouted talking condition. Our results show that HMM2s significantly improve the speaker identification performance compared to the first-order hidden Markov models (HMM1s. The average speaker identification performance under the shouted talking condition based on HMM1s is . On the other hand, the average speaker identification performance based on HMM2s is .
Application of Bayesian Model Selection for Metal Yield Models using ALEGRA and Dakota.
Energy Technology Data Exchange (ETDEWEB)
Portone, Teresa; Niederhaus, John Henry; Sanchez, Jason James; Swiler, Laura Painton
2018-02-01
This report introduces the concepts of Bayesian model selection, which provides a systematic means of calibrating and selecting an optimal model to represent a phenomenon. This has many potential applications, including for comparing constitutive models. The ideas described herein are applied to a model selection problem between different yield models for hardened steel under extreme loading conditions.
A Hybrid Multiple Criteria Decision Making Model for Supplier Selection
Wu, Chung-Min; Hsieh, Ching-Lin; Chang, Kuei-Lun
2013-01-01
The sustainable supplier selection would be the vital part in the management of a sustainable supply chain. In this study, a hybrid multiple criteria decision making (MCDM) model is applied to select optimal supplier. The fuzzy Delphi method, which can lead to better criteria selection, is used to modify criteria. Considering the interdependence among the selection criteria, analytic network process (ANP) is then used to obtain their weights. To avoid calculation and additional pairwise compa...
Astrophysical Model Selection in Gravitational Wave Astronomy
Adams, Matthew R.; Cornish, Neil J.; Littenberg, Tyson B.
2012-01-01
Theoretical studies in gravitational wave astronomy have mostly focused on the information that can be extracted from individual detections, such as the mass of a binary system and its location in space. Here we consider how the information from multiple detections can be used to constrain astrophysical population models. This seemingly simple problem is made challenging by the high dimensionality and high degree of correlation in the parameter spaces that describe the signals, and by the complexity of the astrophysical models, which can also depend on a large number of parameters, some of which might not be directly constrained by the observations. We present a method for constraining population models using a hierarchical Bayesian modeling approach which simultaneously infers the source parameters and population model and provides the joint probability distributions for both. We illustrate this approach by considering the constraints that can be placed on population models for galactic white dwarf binaries using a future space-based gravitational wave detector. We find that a mission that is able to resolve approximately 5000 of the shortest period binaries will be able to constrain the population model parameters, including the chirp mass distribution and a characteristic galaxy disk radius to within a few percent. This compares favorably to existing bounds, where electromagnetic observations of stars in the galaxy constrain disk radii to within 20%.
Modeling and Analysis of Supplier Selection Method Using ...
African Journals Online (AJOL)
However, in these parts of the world the application of tools and models for supplier selection problem is yet to surface and the banking and finance industry here in Ethiopia is no exception. Thus, the purpose of this research was to address supplier selection problem through modeling and application of analytical hierarchy ...
Dealing with selection bias in educational transition models
DEFF Research Database (Denmark)
Holm, Anders; Jæger, Mads Meier
2011-01-01
This paper proposes the bivariate probit selection model (BPSM) as an alternative to the traditional Mare model for analyzing educational transitions. The BPSM accounts for selection on unobserved variables by allowing for unobserved variables which affect the probability of making educational tr...
On Optimal Input Design and Model Selection for Communication Channels
Energy Technology Data Exchange (ETDEWEB)
Li, Yanyan [ORNL; Djouadi, Seddik M [ORNL; Olama, Mohammed M [ORNL
2013-01-01
In this paper, the optimal model (structure) selection and input design which minimize the worst case identification error for communication systems are provided. The problem is formulated using metric complexity theory in a Hilbert space setting. It is pointed out that model selection and input design can be handled independently. Kolmogorov n-width is used to characterize the representation error introduced by model selection, while Gel fand and Time n-widths are used to represent the inherent error introduced by input design. After the model is selected, an optimal input which minimizes the worst case identification error is shown to exist. In particular, it is proven that the optimal model for reducing the representation error is a Finite Impulse Response (FIR) model, and the optimal input is an impulse at the start of the observation interval. FIR models are widely popular in communication systems, such as, in Orthogonal Frequency Division Multiplexing (OFDM) systems.
Python Program to Select HII Region Models
Miller, Clare; Lamarche, Cody; Vishwas, Amit; Stacey, Gordon J.
2016-01-01
HII regions are areas of singly ionized Hydrogen formed by the ionizing radiaiton of upper main sequence stars. The infrared fine-structure line emissions, particularly Oxygen, Nitrogen, and Neon, can give important information about HII regions including gas temperature and density, elemental abundances, and the effective temperature of the stars that form them. The processes involved in calculating this information from observational data are complex. Models, such as those provided in Rubin 1984 and those produced by Cloudy (Ferland et al, 2013) enable one to extract physical parameters from observational data. However, the multitude of search parameters can make sifting through models tedious. I digitized Rubin's models and wrote a Python program that is able to take observed line ratios and their uncertainties and find the Rubin or Cloudy model that best matches the observational data. By creating a Python script that is user friendly and able to quickly sort through models with a high level of accuracy, this work increases efficiency and reduces human error in matching HII region models to observational data.
Ground-water transport model selection and evaluation guidelines
International Nuclear Information System (INIS)
Simmons, C.S.; Cole, C.R.
1983-01-01
Guidelines are being developed to assist potential users with selecting appropriate computer codes for ground-water contaminant transport modeling. The guidelines are meant to assist managers with selecting appropriate predictive models for evaluating either arid or humid low-level radioactive waste burial sites. Evaluation test cases in the form of analytical solutions to fundamental equations and experimental data sets have been identified and recommended to ensure adequate code selection, based on accurate simulation of relevant physical processes. The recommended evaluation procedures will consider certain technical issues related to the present limitations in transport modeling capabilities. A code-selection plan will depend on identifying problem objectives, determining the extent of collectible site-specific data, and developing a site-specific conceptual model for the involved hydrology. Code selection will be predicated on steps for developing an appropriate systems model. This paper will review the progress in developing those guidelines. 12 references
Model and Variable Selection Procedures for Semiparametric Time Series Regression
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Risa Kato
2009-01-01
Full Text Available Semiparametric regression models are very useful for time series analysis. They facilitate the detection of features resulting from external interventions. The complexity of semiparametric models poses new challenges for issues of nonparametric and parametric inference and model selection that frequently arise from time series data analysis. In this paper, we propose penalized least squares estimators which can simultaneously select significant variables and estimate unknown parameters. An innovative class of variable selection procedure is proposed to select significant variables and basis functions in a semiparametric model. The asymptotic normality of the resulting estimators is established. Information criteria for model selection are also proposed. We illustrate the effectiveness of the proposed procedures with numerical simulations.
Methods for model selection in applied science and engineering.
Energy Technology Data Exchange (ETDEWEB)
Field, Richard V., Jr.
2004-10-01
Mathematical models are developed and used to study the properties of complex systems and/or modify these systems to satisfy some performance requirements in just about every area of applied science and engineering. A particular reason for developing a model, e.g., performance assessment or design, is referred to as the model use. Our objective is the development of a methodology for selecting a model that is sufficiently accurate for an intended use. Information on the system being modeled is, in general, incomplete, so that there may be two or more models consistent with the available information. The collection of these models is called the class of candidate models. Methods are developed for selecting the optimal member from a class of candidate models for the system. The optimal model depends on the available information, the selected class of candidate models, and the model use. Classical methods for model selection, including the method of maximum likelihood and Bayesian methods, as well as a method employing a decision-theoretic approach, are formulated to select the optimal model for numerous applications. There is no requirement that the candidate models be random. Classical methods for model selection ignore model use and require data to be available. Examples are used to show that these methods can be unreliable when data is limited. The decision-theoretic approach to model selection does not have these limitations, and model use is included through an appropriate utility function. This is especially important when modeling high risk systems, where the consequences of using an inappropriate model for the system can be disastrous. The decision-theoretic method for model selection is developed and applied for a series of complex and diverse applications. These include the selection of the: (1) optimal order of the polynomial chaos approximation for non-Gaussian random variables and stationary stochastic processes, (2) optimal pressure load model to be
Random effect selection in generalised linear models
DEFF Research Database (Denmark)
Denwood, Matt; Houe, Hans; Forkman, Björn
We analysed abattoir recordings of meat inspection codes with possible relevance to onfarm animal welfare in cattle. Random effects logistic regression models were used to describe individual-level data obtained from 461,406 cattle slaughtered in Denmark. Our results demonstrate that the largest...
The genealogy of samples in models with selection.
Neuhauser, C; Krone, S M
1997-02-01
We introduce the genealogy of a random sample of genes taken from a large haploid population that evolves according to random reproduction with selection and mutation. Without selection, the genealogy is described by Kingman's well-known coalescent process. In the selective case, the genealogy of the sample is embedded in a graph with a coalescing and branching structure. We describe this graph, called the ancestral selection graph, and point out differences and similarities with Kingman's coalescent. We present simulations for a two-allele model with symmetric mutation in which one of the alleles has a selective advantage over the other. We find that when the allele frequencies in the population are already in equilibrium, then the genealogy does not differ much from the neutral case. This is supported by rigorous results. Furthermore, we describe the ancestral selection graph for other selective models with finitely many selection classes, such as the K-allele models, infinitely-many-alleles models. DNA sequence models, and infinitely-many-sites models, and briefly discuss the diploid case.
A kingdom-specific protein domain HMM library for improved annotation of fungal genomes
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Oliver Stephen G
2007-04-01
Full Text Available Abstract Background Pfam is a general-purpose database of protein domain alignments and profile Hidden Markov Models (HMMs, which is very popular for the annotation of sequence data produced by genome sequencing projects. Pfam provides models that are often very general in terms of the taxa that they cover and it has previously been suggested that such general models may lack some of the specificity or selectivity that would be provided by kingdom-specific models. Results Here we present a general approach to create domain libraries of HMMs for sub-taxa of a kingdom. Taking fungal species as an example, we construct a domain library of HMMs (called Fungal Pfam or FPfam using sequences from 30 genomes, consisting of 24 species from the ascomycetes group and two basidiomycetes, Ustilago maydis, a fungal pathogen of maize, and the white rot fungus Phanerochaete chrysosporium. In addition, we include the Microsporidion Encephalitozoon cuniculi, an obligate intracellular parasite, and two non-fungal species, the oomycetes Phytophthora sojae and Phytophthora ramorum, both plant pathogens. We evaluate the performance in terms of coverage against the original 30 genomes used in training FPfam and against five more recently sequenced fungal genomes that can be considered as an independent test set. We show that kingdom-specific models such as FPfam can find instances of both novel and well characterized domains, increases overall coverage and detects more domains per sequence with typically higher bitscores than Pfam for the same domain families. An evaluation of the effect of changing E-values on the coverage shows that the performance of FPfam is consistent over the range of E-values applied. Conclusion Kingdom-specific models are shown to provide improved coverage. However, as the models become more specific, some sequences found by Pfam may be missed by the models in FPfam and some of the families represented in the test set are not present in FPfam
Modeling shape selection of buckled dielectric elastomers
Langham, Jacob; Bense, Hadrien; Barkley, Dwight
2018-02-01
A dielectric elastomer whose edges are held fixed will buckle, given a sufficiently applied voltage, resulting in a nontrivial out-of-plane deformation. We study this situation numerically using a nonlinear elastic model which decouples two of the principal electrostatic stresses acting on an elastomer: normal pressure due to the mutual attraction of oppositely charged electrodes and tangential shear ("fringing") due to repulsion of like charges at the electrode edges. These enter via physically simplified boundary conditions that are applied in a fixed reference domain using a nondimensional approach. The method is valid for small to moderate strains and is straightforward to implement in a generic nonlinear elasticity code. We validate the model by directly comparing the simulated equilibrium shapes with the experiment. For circular electrodes which buckle axisymetrically, the shape of the deflection profile is captured. Annular electrodes of different widths produce azimuthal ripples with wavelengths that match our simulations. In this case, it is essential to compute multiple equilibria because the first model solution obtained by the nonlinear solver (Newton's method) is often not the energetically favored state. We address this using a numerical technique known as "deflation." Finally, we observe the large number of different solutions that may be obtained for the case of a long rectangular strip.
Modeling HIV-1 drug resistance as episodic directional selection.
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Ben Murrell
Full Text Available The evolution of substitutions conferring drug resistance to HIV-1 is both episodic, occurring when patients are on antiretroviral therapy, and strongly directional, with site-specific resistant residues increasing in frequency over time. While methods exist to detect episodic diversifying selection and continuous directional selection, no evolutionary model combining these two properties has been proposed. We present two models of episodic directional selection (MEDS and EDEPS which allow the a priori specification of lineages expected to have undergone directional selection. The models infer the sites and target residues that were likely subject to directional selection, using either codon or protein sequences. Compared to its null model of episodic diversifying selection, MEDS provides a superior fit to most sites known to be involved in drug resistance, and neither one test for episodic diversifying selection nor another for constant directional selection are able to detect as many true positives as MEDS and EDEPS while maintaining acceptable levels of false positives. This suggests that episodic directional selection is a better description of the process driving the evolution of drug resistance.
Variable selection for mixture and promotion time cure rate models.
Masud, Abdullah; Tu, Wanzhu; Yu, Zhangsheng
2016-11-16
Failure-time data with cured patients are common in clinical studies. Data from these studies are typically analyzed with cure rate models. Variable selection methods have not been well developed for cure rate models. In this research, we propose two least absolute shrinkage and selection operators based methods, for variable selection in mixture and promotion time cure models with parametric or nonparametric baseline hazards. We conduct an extensive simulation study to assess the operating characteristics of the proposed methods. We illustrate the use of the methods using data from a study of childhood wheezing. © The Author(s) 2016.
ADAPTIVE LEARNING OF HIDDEN MARKOV MODELS FOR EMOTIONAL SPEECH
Directory of Open Access Journals (Sweden)
A. V. Tkachenia
2014-01-01
Full Text Available An on-line unsupervised algorithm for estimating the hidden Markov models (HMM parame-ters is presented. The problem of hidden Markov models adaptation to emotional speech is solved. To increase the reliability of estimated HMM parameters, a mechanism of forgetting and updating is proposed. A functional block diagram of the hidden Markov models adaptation algorithm is also provided with obtained results, which improve the efficiency of emotional speech recognition.
Partner Selection Optimization Model of Agricultural Enterprises in Supply Chain
Feipeng Guo; Qibei Lu
2013-01-01
With more and more importance of correctly selecting partners in supply chain of agricultural enterprises, a large number of partner evaluation techniques are widely used in the field of agricultural science research. This study established a partner selection model to optimize the issue of agricultural supply chain partner selection. Firstly, it constructed a comprehensive evaluation index system after analyzing the real characteristics of agricultural supply chain. Secondly, a heuristic met...
Effect of Model Selection on Computed Water Balance Components
Jhorar, R.K.; Smit, A.A.M.F.R.; Roest, C.W.J.
2009-01-01
Soil water flow modelling approaches as used in four selected on-farm water management models, namely CROPWAT. FAIDS, CERES and SWAP, are compared through numerical experiments. The soil water simulation approaches used in the first three models are reformulated to incorporate ail evapotranspiration
Ensembling Variable Selectors by Stability Selection for the Cox Model
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Qing-Yan Yin
2017-01-01
Full Text Available As a pivotal tool to build interpretive models, variable selection plays an increasingly important role in high-dimensional data analysis. In recent years, variable selection ensembles (VSEs have gained much interest due to their many advantages. Stability selection (Meinshausen and Bühlmann, 2010, a VSE technique based on subsampling in combination with a base algorithm like lasso, is an effective method to control false discovery rate (FDR and to improve selection accuracy in linear regression models. By adopting lasso as a base learner, we attempt to extend stability selection to handle variable selection problems in a Cox model. According to our experience, it is crucial to set the regularization region Λ in lasso and the parameter λmin properly so that stability selection can work well. To the best of our knowledge, however, there is no literature addressing this problem in an explicit way. Therefore, we first provide a detailed procedure to specify Λ and λmin. Then, some simulated and real-world data with various censoring rates are used to examine how well stability selection performs. It is also compared with several other variable selection approaches. Experimental results demonstrate that it achieves better or competitive performance in comparison with several other popular techniques.
Elementary Teachers' Selection and Use of Visual Models
Lee, Tammy D.; Gail Jones, M.
2018-02-01
As science grows in complexity, science teachers face an increasing challenge of helping students interpret models that represent complex science systems. Little is known about how teachers select and use models when planning lessons. This mixed methods study investigated the pedagogical approaches and visual models used by elementary in-service and preservice teachers in the development of a science lesson about a complex system (e.g., water cycle). Sixty-seven elementary in-service and 69 elementary preservice teachers completed a card sort task designed to document the types of visual models (e.g., images) that teachers choose when planning science instruction. Quantitative and qualitative analyses were conducted to analyze the card sort task. Semistructured interviews were conducted with a subsample of teachers to elicit the rationale for image selection. Results from this study showed that both experienced in-service teachers and novice preservice teachers tended to select similar models and use similar rationales for images to be used in lessons. Teachers tended to select models that were aesthetically pleasing and simple in design and illustrated specific elements of the water cycle. The results also showed that teachers were not likely to select images that represented the less obvious dimensions of the water cycle. Furthermore, teachers selected visual models more as a pedagogical tool to illustrate specific elements of the water cycle and less often as a tool to promote student learning related to complex systems.
Validation of elk resource selection models with spatially independent data
Priscilla K. Coe; Bruce K. Johnson; Michael J. Wisdom; John G. Cook; Marty Vavra; Ryan M. Nielson
2011-01-01
Knowledge of how landscape features affect wildlife resource use is essential for informed management. Resource selection functions often are used to make and validate predictions about landscape use; however, resource selection functions are rarely validated with data from landscapes independent of those from which the models were built. This problem has severely...
A Working Model of Natural Selection Illustrated by Table Tennis
Dinc, Muhittin; Kilic, Selda; Aladag, Caner
2013-01-01
Natural selection is one of the most important topics in biology and it helps to clarify the variety and complexity of organisms. However, students in almost every stage of education find it difficult to understand the mechanism of natural selection and they can develop misconceptions about it. This article provides an active model of natural…
Augmented Self-Modeling as an Intervention for Selective Mutism
Kehle, Thomas J.; Bray, Melissa A.; Byer-Alcorace, Gabriel F.; Theodore, Lea A.; Kovac, Lisa M.
2012-01-01
Selective mutism is a rare disorder that is difficult to treat. It is often associated with oppositional defiant behavior, particularly in the home setting, social phobia, and, at times, autism spectrum disorder characteristics. The augmented self-modeling treatment has been relatively successful in promoting rapid diminishment of selective mutism…
Robust Decision-making Applied to Model Selection
Energy Technology Data Exchange (ETDEWEB)
Hemez, Francois M. [Los Alamos National Laboratory
2012-08-06
The scientific and engineering communities are relying more and more on numerical models to simulate ever-increasingly complex phenomena. Selecting a model, from among a family of models that meets the simulation requirements, presents a challenge to modern-day analysts. To address this concern, a framework is adopted anchored in info-gap decision theory. The framework proposes to select models by examining the trade-offs between prediction accuracy and sensitivity to epistemic uncertainty. The framework is demonstrated on two structural engineering applications by asking the following question: Which model, of several numerical models, approximates the behavior of a structure when parameters that define each of those models are unknown? One observation is that models that are nominally more accurate are not necessarily more robust, and their accuracy can deteriorate greatly depending upon the assumptions made. It is posited that, as reliance on numerical models increases, establishing robustness will become as important as demonstrating accuracy.
Target Selection Models with Preference Variation Between Offenders
Townsley, Michael; Birks, Daniel; Ruiter, Stijn; Bernasco, Wim; White, Gentry
2016-01-01
Objectives: This study explores preference variation in location choice strategies of residential burglars. Applying a model of offender target selection that is grounded in assertions of the routine activity approach, rational choice perspective, crime pattern and social disorganization theories,
Akaike information criterion to select well-fit resist models
Burbine, Andrew; Fryer, David; Sturtevant, John
2015-03-01
In the field of model design and selection, there is always a risk that a model is over-fit to the data used to train the model. A model is well suited when it describes the physical system and not the stochastic behavior of the particular data collected. K-fold cross validation is a method to check this potential over-fitting to the data by calibrating with k-number of folds in the data, typically between 4 and 10. Model training is a computationally expensive operation, however, and given a wide choice of candidate models, calibrating each one repeatedly becomes prohibitively time consuming. Akaike information criterion (AIC) is an information-theoretic approach to model selection based on the maximized log-likelihood for a given model that only needs a single calibration per model. It is used in this study to demonstrate model ranking and selection among compact resist modelforms that have various numbers and types of terms to describe photoresist behavior. It is shown that there is a good correspondence of AIC to K-fold cross validation in selecting the best modelform, and it is further shown that over-fitting is, in most cases, not indicated. In modelforms with more than 40 fitting parameters, the size of the calibration data set benefits from additional parameters, statistically validating the model complexity.
A risk assessment model for selecting cloud service providers
Cayirci, Erdal; Garaga, Alexandr; Santana de Oliveira, Anderson; Roudier, Yves
2016-01-01
The Cloud Adoption Risk Assessment Model is designed to help cloud customers in assessing the risks that they face by selecting a specific cloud service provider. It evaluates background information obtained from cloud customers and cloud service providers to analyze various risk scenarios. This facilitates decision making an selecting the cloud service provider with the most preferable risk profile based on aggregated risks to security, privacy, and service delivery. Based on this model we ...
SELECTION MOMENTS AND GENERALIZED METHOD OF MOMENTS FOR HETEROSKEDASTIC MODELS
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Constantin ANGHELACHE
2016-06-01
Full Text Available In this paper, the authors describe the selection methods for moments and the application of the generalized moments method for the heteroskedastic models. The utility of GMM estimators is found in the study of the financial market models. The selection criteria for moments are applied for the efficient estimation of GMM for univariate time series with martingale difference errors, similar to those studied so far by Kuersteiner.
Model Selection in Continuous Test Norming With GAMLSS.
Voncken, Lieke; Albers, Casper J; Timmerman, Marieke E
2017-06-01
To compute norms from reference group test scores, continuous norming is preferred over traditional norming. A suitable continuous norming approach for continuous data is the use of the Box-Cox Power Exponential model, which is found in the generalized additive models for location, scale, and shape. Applying the Box-Cox Power Exponential model for test norming requires model selection, but it is unknown how well this can be done with an automatic selection procedure. In a simulation study, we compared the performance of two stepwise model selection procedures combined with four model-fit criteria (Akaike information criterion, Bayesian information criterion, generalized Akaike information criterion (3), cross-validation), varying data complexity, sampling design, and sample size in a fully crossed design. The new procedure combined with one of the generalized Akaike information criterion was the most efficient model selection procedure (i.e., required the smallest sample size). The advocated model selection procedure is illustrated with norming data of an intelligence test.
Selection Criteria in Regime Switching Conditional Volatility Models
Directory of Open Access Journals (Sweden)
Thomas Chuffart
2015-05-01
Full Text Available A large number of nonlinear conditional heteroskedastic models have been proposed in the literature. Model selection is crucial to any statistical data analysis. In this article, we investigate whether the most commonly used selection criteria lead to choice of the right specification in a regime switching framework. We focus on two types of models: the Logistic Smooth Transition GARCH and the Markov-Switching GARCH models. Simulation experiments reveal that information criteria and loss functions can lead to misspecification ; BIC sometimes indicates the wrong regime switching framework. Depending on the Data Generating Process used in the experiments, great care is needed when choosing a criterion.
A guide to Bayesian model selection for ecologists
Hooten, Mevin B.; Hobbs, N.T.
2015-01-01
The steady upward trend in the use of model selection and Bayesian methods in ecological research has made it clear that both approaches to inference are important for modern analysis of models and data. However, in teaching Bayesian methods and in working with our research colleagues, we have noticed a general dissatisfaction with the available literature on Bayesian model selection and multimodel inference. Students and researchers new to Bayesian methods quickly find that the published advice on model selection is often preferential in its treatment of options for analysis, frequently advocating one particular method above others. The recent appearance of many articles and textbooks on Bayesian modeling has provided welcome background on relevant approaches to model selection in the Bayesian framework, but most of these are either very narrowly focused in scope or inaccessible to ecologists. Moreover, the methodological details of Bayesian model selection approaches are spread thinly throughout the literature, appearing in journals from many different fields. Our aim with this guide is to condense the large body of literature on Bayesian approaches to model selection and multimodel inference and present it specifically for quantitative ecologists as neutrally as possible. We also bring to light a few important and fundamental concepts relating directly to model selection that seem to have gone unnoticed in the ecological literature. Throughout, we provide only a minimal discussion of philosophy, preferring instead to examine the breadth of approaches as well as their practical advantages and disadvantages. This guide serves as a reference for ecologists using Bayesian methods, so that they can better understand their options and can make an informed choice that is best aligned with their goals for inference.
The Use of Evolution in a Central Action Selection Model
Directory of Open Access Journals (Sweden)
F. Montes-Gonzalez
2007-01-01
Full Text Available The use of effective central selection provides flexibility in design by offering modularity and extensibility. In earlier papers we have focused on the development of a simple centralized selection mechanism. Our current goal is to integrate evolutionary methods in the design of non-sequential behaviours and the tuning of specific parameters of the selection model. The foraging behaviour of an animal robot (animat has been modelled in order to integrate the sensory information from the robot to perform selection that is nearly optimized by the use of genetic algorithms. In this paper we present how selection through optimization finally arranges the pattern of presented behaviours for the foraging task. Hence, the execution of specific parts in a behavioural pattern may be ruled out by the tuning of these parameters. Furthermore, the intensive use of colour segmentation from a colour camera for locating a cylinder sets a burden on the calculations carried out by the genetic algorithm.
A Hybrid Multiple Criteria Decision Making Model for Supplier Selection
Directory of Open Access Journals (Sweden)
Chung-Min Wu
2013-01-01
Full Text Available The sustainable supplier selection would be the vital part in the management of a sustainable supply chain. In this study, a hybrid multiple criteria decision making (MCDM model is applied to select optimal supplier. The fuzzy Delphi method, which can lead to better criteria selection, is used to modify criteria. Considering the interdependence among the selection criteria, analytic network process (ANP is then used to obtain their weights. To avoid calculation and additional pairwise comparisons of ANP, a technique for order preference by similarity to ideal solution (TOPSIS is used to rank the alternatives. The use of a combination of the fuzzy Delphi method, ANP, and TOPSIS, proposing an MCDM model for supplier selection, and applying these to a real case are the unique features of this study.
Variable selection in Logistic regression model with genetic algorithm.
Zhang, Zhongheng; Trevino, Victor; Hoseini, Sayed Shahabuddin; Belciug, Smaranda; Boopathi, Arumugam Manivanna; Zhang, Ping; Gorunescu, Florin; Subha, Velappan; Dai, Songshi
2018-02-01
Variable or feature selection is one of the most important steps in model specification. Especially in the case of medical-decision making, the direct use of a medical database, without a previous analysis and preprocessing step, is often counterproductive. In this way, the variable selection represents the method of choosing the most relevant attributes from the database in order to build a robust learning models and, thus, to improve the performance of the models used in the decision process. In biomedical research, the purpose of variable selection is to select clinically important and statistically significant variables, while excluding unrelated or noise variables. A variety of methods exist for variable selection, but none of them is without limitations. For example, the stepwise approach, which is highly used, adds the best variable in each cycle generally producing an acceptable set of variables. Nevertheless, it is limited by the fact that it commonly trapped in local optima. The best subset approach can systematically search the entire covariate pattern space, but the solution pool can be extremely large with tens to hundreds of variables, which is the case in nowadays clinical data. Genetic algorithms (GA) are heuristic optimization approaches and can be used for variable selection in multivariable regression models. This tutorial paper aims to provide a step-by-step approach to the use of GA in variable selection. The R code provided in the text can be extended and adapted to other data analysis needs.
Statistical model selection with “Big Data”
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Jurgen A. Doornik
2015-12-01
Full Text Available Big Data offer potential benefits for statistical modelling, but confront problems including an excess of false positives, mistaking correlations for causes, ignoring sampling biases and selecting by inappropriate methods. We consider the many important requirements when searching for a data-based relationship using Big Data, and the possible role of Autometrics in that context. Paramount considerations include embedding relationships in general initial models, possibly restricting the number of variables to be selected over by non-statistical criteria (the formulation problem, using good quality data on all variables, analyzed with tight significance levels by a powerful selection procedure, retaining available theory insights (the selection problem while testing for relationships being well specified and invariant to shifts in explanatory variables (the evaluation problem, using a viable approach that resolves the computational problem of immense numbers of possible models.
Multicriteria framework for selecting a process modelling language
Scanavachi Moreira Campos, Ana Carolina; Teixeira de Almeida, Adiel
2016-01-01
The choice of process modelling language can affect business process management (BPM) since each modelling language shows different features of a given process and may limit the ways in which a process can be described and analysed. However, choosing the appropriate modelling language for process modelling has become a difficult task because of the availability of a large number modelling languages and also due to the lack of guidelines on evaluating, and comparing languages so as to assist in selecting the most appropriate one. This paper proposes a framework for selecting a modelling language in accordance with the purposes of modelling. This framework is based on the semiotic quality framework (SEQUAL) for evaluating process modelling languages and a multicriteria decision aid (MCDA) approach in order to select the most appropriate language for BPM. This study does not attempt to set out new forms of assessment and evaluation criteria, but does attempt to demonstrate how two existing approaches can be combined so as to solve the problem of selection of modelling language. The framework is described in this paper and then demonstrated by means of an example. Finally, the advantages and disadvantages of using SEQUAL and MCDA in an integrated manner are discussed.
Optimal experiment design for model selection in biochemical networks.
Vanlier, Joep; Tiemann, Christian A; Hilbers, Peter A J; van Riel, Natal A W
2014-02-20
Mathematical modeling is often used to formalize hypotheses on how a biochemical network operates by discriminating between competing models. Bayesian model selection offers a way to determine the amount of evidence that data provides to support one model over the other while favoring simple models. In practice, the amount of experimental data is often insufficient to make a clear distinction between competing models. Often one would like to perform a new experiment which would discriminate between competing hypotheses. We developed a novel method to perform Optimal Experiment Design to predict which experiments would most effectively allow model selection. A Bayesian approach is applied to infer model parameter distributions. These distributions are sampled and used to simulate from multivariate predictive densities. The method is based on a k-Nearest Neighbor estimate of the Jensen Shannon divergence between the multivariate predictive densities of competing models. We show that the method successfully uses predictive differences to enable model selection by applying it to several test cases. Because the design criterion is based on predictive distributions, which can be computed for a wide range of model quantities, the approach is very flexible. The method reveals specific combinations of experiments which improve discriminability even in cases where data is scarce. The proposed approach can be used in conjunction with existing Bayesian methodologies where (approximate) posteriors have been determined, making use of relations that exist within the inferred posteriors.
Quantile hydrologic model selection and model structure deficiency assessment : 1. Theory
Pande, S.
2013-01-01
A theory for quantile based hydrologic model selection and model structure deficiency assessment is presented. The paper demonstrates that the degree to which a model selection problem is constrained by the model structure (measured by the Lagrange multipliers of the constraints) quantifies
Fuzzy Investment Portfolio Selection Models Based on Interval Analysis Approach
Directory of Open Access Journals (Sweden)
Haifeng Guo
2012-01-01
Full Text Available This paper employs fuzzy set theory to solve the unintuitive problem of the Markowitz mean-variance (MV portfolio model and extend it to a fuzzy investment portfolio selection model. Our model establishes intervals for expected returns and risk preference, which can take into account investors' different investment appetite and thus can find the optimal resolution for each interval. In the empirical part, we test this model in Chinese stocks investment and find that this model can fulfill different kinds of investors’ objectives. Finally, investment risk can be decreased when we add investment limit to each stock in the portfolio, which indicates our model is useful in practice.
Development of an Environment for Software Reliability Model Selection
1992-09-01
now is directed to other related problems such as tools for model selection, multiversion programming, and software fault tolerance modeling... multiversion programming, 7. Hlardware can be repaired by spare modules, which is not. the case for software, 2-6 N. Preventive maintenance is very important
Testing exclusion restrictions and additive separability in sample selection models
DEFF Research Database (Denmark)
Huber, Martin; Mellace, Giovanni
2014-01-01
Standard sample selection models with non-randomly censored outcomes assume (i) an exclusion restriction (i.e., a variable affecting selection, but not the outcome) and (ii) additive separability of the errors in the selection process. This paper proposes tests for the joint satisfaction of these......Standard sample selection models with non-randomly censored outcomes assume (i) an exclusion restriction (i.e., a variable affecting selection, but not the outcome) and (ii) additive separability of the errors in the selection process. This paper proposes tests for the joint satisfaction...... of these assumptions by applying the approach of Huber and Mellace (Testing instrument validity for LATE identification based on inequality moment constraints, 2011) (for testing instrument validity under treatment endogeneity) to the sample selection framework. We show that the exclusion restriction and additive...... separability imply two testable inequality constraints that come from both point identifying and bounding the outcome distribution of the subpopulation that is always selected/observed. We apply the tests to two variables for which the exclusion restriction is frequently invoked in female wage regressions: non...
Selection Bias in Educational Transition Models: Theory and Empirical Evidence
DEFF Research Database (Denmark)
Holm, Anders; Jæger, Mads
Most studies using Mare’s (1980, 1981) seminal model of educational transitions find that the effect of family background decreases across transitions. Recently, Cameron and Heckman (1998, 2001) have argued that the “waning coefficients” in the Mare model are driven by selection on unobserved...... the United States, United Kingdom, Denmark, and the Netherlands shows that when we take selection into account the effect of family background variables on educational transitions is largely constant across transitions. We also discuss several difficulties in estimating educational transition models which...
Novel web service selection model based on discrete group search.
Zhai, Jie; Shao, Zhiqing; Guo, Yi; Zhang, Haiteng
2014-01-01
In our earlier work, we present a novel formal method for the semiautomatic verification of specifications and for describing web service composition components by using abstract concepts. After verification, the instantiations of components were selected to satisfy the complex service performance constraints. However, selecting an optimal instantiation, which comprises different candidate services for each generic service, from a large number of instantiations is difficult. Therefore, we present a new evolutionary approach on the basis of the discrete group search service (D-GSS) model. With regard to obtaining the optimal multiconstraint instantiation of the complex component, the D-GSS model has competitive performance compared with other service selection models in terms of accuracy, efficiency, and ability to solve high-dimensional service composition component problems. We propose the cost function and the discrete group search optimizer (D-GSO) algorithm and study the convergence of the D-GSS model through verification and test cases.
Selection of climate change scenario data for impact modelling
DEFF Research Database (Denmark)
Sloth Madsen, M; Fox Maule, C; MacKellar, N
2012-01-01
Impact models investigating climate change effects on food safety often need detailed climate data. The aim of this study was to select climate change projection data for selected crop phenology and mycotoxin impact models. Using the ENSEMBLES database of climate model output, this study...... illustrates how the projected climate change signal of important variables as temperature, precipitation and relative humidity depends on the choice of the climate model. Using climate change projections from at least two different climate models is recommended to account for model uncertainty. To make...... the climate projections suitable for impact analysis at the local scale a weather generator approach was adopted. As the weather generator did not treat all the necessary variables, an ad-hoc statistical method was developed to synthesise realistic values of missing variables. The method is presented...
Adverse Selection Models with Three States of Nature
Directory of Open Access Journals (Sweden)
Daniela MARINESCU
2011-02-01
Full Text Available In the paper we analyze an adverse selection model with three states of nature, where both the Principal and the Agent are risk neutral. When solving the model, we use the informational rents and the efforts as variables. We derive the optimal contract in the situation of asymmetric information. The paper ends with the characteristics of the optimal contract and the main conclusions of the model.
A SUPPLIER SELECTION MODEL FOR SOFTWARE DEVELOPMENT OUTSOURCING
Directory of Open Access Journals (Sweden)
Hancu Lucian-Viorel
2010-12-01
Full Text Available This paper presents a multi-criteria decision making model used for supplier selection for software development outsourcing on e-marketplaces. This model can be used in auctions. The supplier selection process becomes complex and difficult on last twenty years since the Internet plays an important role in business management. Companies have to concentrate their efforts on their core activities and the others activities should be realized by outsourcing. They can achieve significant cost reduction by using e-marketplaces in their purchase process and by using decision support systems on supplier selection. In the literature were proposed many approaches for supplier evaluation and selection process. The performance of potential suppliers is evaluated using multi criteria decision making methods rather than considering a single factor cost.
Modeling quality attributes and metrics for web service selection
Oskooei, Meysam Ahmadi; Daud, Salwani binti Mohd; Chua, Fang-Fang
2014-06-01
Since the service-oriented architecture (SOA) has been designed to develop the system as a distributed application, the service selection has become a vital aspect of service-oriented computing (SOC). Selecting the appropriate web service with respect to quality of service (QoS) through using mathematical solution for optimization of problem turns the service selection problem into a common concern for service users. Nowadays, number of web services that provide the same functionality is increased and selection of services from a set of alternatives which differ in quality parameters can be difficult for service consumers. In this paper, a new model for QoS attributes and metrics is proposed to provide a suitable solution for optimizing web service selection and composition with low complexity.
[On selection criteria in spatially distributed models of competition].
Il'ichev, V G; Il'icheva, O A
2014-01-01
Discrete models of competitors (initial population and mutants) are considered in which reproduction is set by increasing and concave function, and migration in the space consisting of a set of areas, is described by a Markov matrix. This allows the use of the theory of monotonous operators to study problems of selection, coexistence and stability. It is shown that the higher is the number of areas, more and more severe constraints of selective advantage to initial population are required.
Comparing the staffing models of outsourcing in selected companies
Chaloupková, Věra
2010-01-01
This thesis deals with problems of takeover of employees in outsourcing. The capital purpose is to compare the staffing model of outsourcing in selected companies. To compare in selected companies I chose multi-criteria analysis. This thesis is dividend into six chapters. The first charter is devoted to the theoretical part. In this charter describes the basic concepts as outsourcing, personal aspects, phase of the outsourcing projects, communications and culture. The rest of thesis is devote...
Economic assessment model architecture for AGC/AVLIS selection
International Nuclear Information System (INIS)
Hoglund, R.L.
1984-01-01
The economic assessment model architecture described provides the flexibility and completeness in economic analysis that the selection between AGC and AVLIS demands. Process models which are technology-specific will provide the first-order responses of process performance and cost to variations in process parameters. The economics models can be used to test the impacts of alternative deployment scenarios for a technology. Enterprise models provide global figures of merit for evaluating the DOE perspective on the uranium enrichment enterprise, and business analysis models compute the financial parameters from the private investor's viewpoint
Lightweight Graphical Models for Selectivity Estimation Without Independence Assumptions
DEFF Research Database (Denmark)
Tzoumas, Kostas; Deshpande, Amol; Jensen, Christian S.
2011-01-01
, propagated exponentially, can lead to severely sub-optimal plans. Modern optimizers typically maintain one-dimensional statistical summaries and make the attribute value independence and join uniformity assumptions for efficiently estimating selectivities. Therefore, selectivity estimation errors in today......’s optimizers are frequently caused by missed correlations between attributes. We present a selectivity estimation approach that does not make the independence assumptions. By carefully using concepts from the field of graphical models, we are able to factor the joint probability distribution of all...
Genetic signatures of natural selection in a model invasive ascidian
Lin, Yaping; Chen, Yiyong; Yi, Changho; Fong, Jonathan J.; Kim, Won; Rius, Marc; Zhan, Aibin
2017-03-01
Invasive species represent promising models to study species’ responses to rapidly changing environments. Although local adaptation frequently occurs during contemporary range expansion, the associated genetic signatures at both population and genomic levels remain largely unknown. Here, we use genome-wide gene-associated microsatellites to investigate genetic signatures of natural selection in a model invasive ascidian, Ciona robusta. Population genetic analyses of 150 individuals sampled in Korea, New Zealand, South Africa and Spain showed significant genetic differentiation among populations. Based on outlier tests, we found high incidence of signatures of directional selection at 19 loci. Hitchhiking mapping analyses identified 12 directional selective sweep regions, and all selective sweep windows on chromosomes were narrow (~8.9 kb). Further analyses indentified 132 candidate genes under selection. When we compared our genetic data and six crucial environmental variables, 16 putatively selected loci showed significant correlation with these environmental variables. This suggests that the local environmental conditions have left significant signatures of selection at both population and genomic levels. Finally, we identified “plastic” genomic regions and genes that are promising regions to investigate evolutionary responses to rapid environmental change in C. robusta.
Learning Model Structure from Data : an Application to On-Line Handwriting
Binsztok, Henri
2005-01-01
We present a learning strategy for Hidden Markov Models that may be used to cluster handwriting sequences or to learn a character model by identifying its main writing styles. Our approach aims at learning both the structure and parameters of a Hidden Markov Model (HMM) from the data. A byproduct of this learning strategy is the ability to cluster signals and identify allograph. We provide experimental results on artificial data that demonstrate the possibility to learn from data HMM paramete...
Ecohydrological model parameter selection for stream health evaluation.
Woznicki, Sean A; Nejadhashemi, A Pouyan; Ross, Dennis M; Zhang, Zhen; Wang, Lizhu; Esfahanian, Abdol-Hossein
2015-04-01
Variable selection is a critical step in development of empirical stream health prediction models. This study develops a framework for selecting important in-stream variables to predict four measures of biological integrity: total number of Ephemeroptera, Plecoptera, and Trichoptera (EPT) taxa, family index of biotic integrity (FIBI), Hilsenhoff biotic integrity (HBI), and fish index of biotic integrity (IBI). Over 200 flow regime and water quality variables were calculated using the Hydrologic Index Tool (HIT) and Soil and Water Assessment Tool (SWAT). Streams of the River Raisin watershed in Michigan were grouped using the Strahler stream classification system (orders 1-3 and orders 4-6), k-means clustering technique (two clusters: C1 and C2), and all streams (one grouping). For each grouping, variable selection was performed using Bayesian variable selection, principal component analysis, and Spearman's rank correlation. Following selection of best variable sets, models were developed to predict the measures of biological integrity using adaptive-neuro fuzzy inference systems (ANFIS), a technique well-suited to complex, nonlinear ecological problems. Multiple unique variable sets were identified, all which differed by selection method and stream grouping. Final best models were mostly built using the Bayesian variable selection method. The most effective stream grouping method varied by health measure, although k-means clustering and grouping by stream order were always superior to models built without grouping. Commonly selected variables were related to streamflow magnitude, rate of change, and seasonal nitrate concentration. Each best model was effective in simulating stream health observations, with EPT taxa validation R2 ranging from 0.67 to 0.92, FIBI ranging from 0.49 to 0.85, HBI from 0.56 to 0.75, and fish IBI at 0.99 for all best models. The comprehensive variable selection and modeling process proposed here is a robust method that extends our
Financial applications of a Tabu search variable selection model
Directory of Open Access Journals (Sweden)
Zvi Drezner
2001-01-01
Full Text Available We illustrate how a comparatively new technique, a Tabu search variable selection model [Drezner, Marcoulides and Salhi (1999], can be applied efficiently within finance when the researcher must select a subset of variables from among the whole set of explanatory variables under consideration. Several types of problems in finance, including corporate and personal bankruptcy prediction, mortgage and credit scoring, and the selection of variables for the Arbitrage Pricing Model, require the researcher to select a subset of variables from a larger set. In order to demonstrate the usefulness of the Tabu search variable selection model, we: (1 illustrate its efficiency in comparison to the main alternative search procedures, such as stepwise regression and the Maximum R2 procedure, and (2 show how a version of the Tabu search procedure may be implemented when attempting to predict corporate bankruptcy. We accomplish (2 by indicating that a Tabu Search procedure increases the predictability of corporate bankruptcy by up to 10 percentage points in comparison to Altman's (1968 Z-Score model.
Selecting an appropriate genetic evaluation model for selection in a developing dairy sector
McGill, D.M.; Mulder, H.A.; Thomson, P.C.; Lievaart, J.J.
2014-01-01
This study aimed to identify genetic evaluation models (GEM) to accurately select cattle for milk production when only limited data are available. It is based on a data set from the Pakistani Sahiwal progeny testing programme which includes records from five government herds, each consisting of 100
The Properties of Model Selection when Retaining Theory Variables
DEFF Research Database (Denmark)
Hendry, David F.; Johansen, Søren
Economic theories are often fitted directly to data to avoid possible model selection biases. We show that embedding a theory model that specifies the correct set of m relevant exogenous variables, x{t}, within the larger set of m+k candidate variables, (x{t},w{t}), then selection over the second...... set by their statistical significance can be undertaken without affecting the estimator distribution of the theory parameters. This strategy returns the theory-parameter estimates when the theory is correct, yet protects against the theory being under-specified because some w{t} are relevant....
Evaluating bacterial gene-finding HMM structures as probabilistic logic programs
DEFF Research Database (Denmark)
Mørk, Søren; Holmes, Ian
2012-01-01
, a probabilistic dialect of Prolog. Results: We evaluate Hidden Markov Model structures for bacterial protein-coding gene potential, including a simple null model structure, three structures based on existing bacterial gene finders and two novel model structures. We test standard versions as well as ADPH length...
Selecting an optimal mixed products using grey relationship model
Directory of Open Access Journals (Sweden)
Farshad Faezy Razi
2013-06-01
Full Text Available This paper presents an integrated supplier selection and inventory management using grey relationship model (GRM as well as multi-objective decision making process. The proposed model of this paper first ranks different suppliers based on GRM technique and then determines the optimum level of inventory by considering different objectives. To show the implementation of the proposed model, we use some benchmark data presented by Talluri and Baker [Talluri, S., & Baker, R. C. (2002. A multi-phase mathematical programming approach for effective supply chain design. European Journal of Operational Research, 141(3, 544-558.]. The preliminary results indicate that the proposed model of this paper is capable of handling different criteria for supplier selection.
Uniform design based SVM model selection for face recognition
Li, Weihong; Liu, Lijuan; Gong, Weiguo
2010-02-01
Support vector machine (SVM) has been proved to be a powerful tool for face recognition. The generalization capacity of SVM depends on the model with optimal hyperparameters. The computational cost of SVM model selection results in application difficulty in face recognition. In order to overcome the shortcoming, we utilize the advantage of uniform design--space filling designs and uniformly scattering theory to seek for optimal SVM hyperparameters. Then we propose a face recognition scheme based on SVM with optimal model which obtained by replacing the grid and gradient-based method with uniform design. The experimental results on Yale and PIE face databases show that the proposed method significantly improves the efficiency of SVM model selection.
Sample selection and taste correlation in discrete choice transport modelling
DEFF Research Database (Denmark)
Mabit, Stefan Lindhard
2008-01-01
of taste correlation in willingness-to-pay estimation are presented. The first contribution addresses how to incorporate taste correlation in the estimation of the value of travel time for public transport. Given a limited dataset the approach taken is to use theory on the value of travel time as guidance...... many issues that deserve attention. This thesis investigates how sample selection can affect estimation of discrete choice models and how taste correlation should be incorporated into applied mixed logit estimation. Sampling in transport modelling is often based on an observed trip. This may cause...... a sample to be choice-based or governed by a self-selection mechanism. In both cases, there is a possibility that sampling affects the estimation of a population model. It was established in the seventies how choice-based sampling affects the estimation of multinomial logit models. The thesis examines...
Spatial Fleming-Viot models with selection and mutation
Dawson, Donald A
2014-01-01
This book constructs a rigorous framework for analysing selected phenomena in evolutionary theory of populations arising due to the combined effects of migration, selection and mutation in a spatial stochastic population model, namely the evolution towards fitter and fitter types through punctuated equilibria. The discussion is based on a number of new methods, in particular multiple scale analysis, nonlinear Markov processes and their entrance laws, atomic measure-valued evolutions and new forms of duality (for state-dependent mutation and multitype selection) which are used to prove ergodic theorems in this context and are applicable for many other questions and renormalization analysis for a variety of phenomena (stasis, punctuated equilibrium, failure of naive branching approximations, biodiversity) which occur due to the combination of rare mutation, mutation, resampling, migration and selection and make it necessary to mathematically bridge the gap (in the limit) between time and space scales.
Evidence accumulation as a model for lexical selection.
Anders, R; Riès, S; van Maanen, L; Alario, F X
2015-11-01
We propose and demonstrate evidence accumulation as a plausible theoretical and/or empirical model for the lexical selection process of lexical retrieval. A number of current psycholinguistic theories consider lexical selection as a process related to selecting a lexical target from a number of alternatives, which each have varying activations (or signal supports), that are largely resultant of an initial stimulus recognition. We thoroughly present a case for how such a process may be theoretically explained by the evidence accumulation paradigm, and we demonstrate how this paradigm can be directly related or combined with conventional psycholinguistic theory and their simulatory instantiations (generally, neural network models). Then with a demonstrative application on a large new real data set, we establish how the empirical evidence accumulation approach is able to provide parameter results that are informative to leading psycholinguistic theory, and that motivate future theoretical development. Copyright © 2015 Elsevier Inc. All rights reserved.
Integrated model for supplier selection and performance evaluation
Directory of Open Access Journals (Sweden)
Borges de Araújo, Maria Creuza
2015-08-01
Full Text Available This paper puts forward a model for selecting suppliers and evaluating the performance of those already working with a company. A simulation was conducted in a food industry. This sector has high significance in the economy of Brazil. The model enables the phases of selecting and evaluating suppliers to be integrated. This is important so that a company can have partnerships with suppliers who are able to meet their needs. Additionally, a group method is used to enable managers who will be affected by this decision to take part in the selection stage. Finally, the classes resulting from the performance evaluation are shown to support the contractor in choosing the most appropriate relationship with its suppliers.
The Selection of ARIMA Models with or without Regressors
DEFF Research Database (Denmark)
Johansen, Søren; Riani, Marco; Atkinson, Anthony C.
We develop a $C_{p}$ statistic for the selection of regression models with stationary and nonstationary ARIMA error term. We derive the asymptotic theory of the maximum likelihood estimators and show they are consistent and asymptotically Gaussian. We also prove that the distribution of the sum o...
Selecting candidate predictor variables for the modelling of post ...
African Journals Online (AJOL)
Selecting candidate predictor variables for the modelling of post-discharge mortality from sepsis: a protocol development project. Afri. Health Sci. .... Initial list of candidate predictor variables, N=17. Clinical. Laboratory. Social/Demographic. Vital signs (HR, RR, BP, T). Hemoglobin. Age. Oxygen saturation. Blood culture. Sex.
Computationally efficient thermal-mechanical modelling of selective laser melting
Yang, Y.; Ayas, C.; Brabazon, Dermot; Naher, Sumsun; Ul Ahad, Inam
2017-01-01
The Selective laser melting (SLM) is a powder based additive manufacturing (AM) method to produce high density metal parts with complex topology. However, part distortions and accompanying residual stresses deteriorates the mechanical reliability of SLM products. Modelling of the SLM process is
Multivariate time series modeling of selected childhood diseases in ...
African Journals Online (AJOL)
This paper is focused on modeling the five most prevalent childhood diseases in Akwa Ibom State using a multivariate approach to time series. An aggregate of 78,839 reported cases of malaria, upper respiratory tract infection (URTI), Pneumonia, anaemia and tetanus were extracted from five randomly selected hospitals in ...
Model selection for the extraction of movement primitives
Directory of Open Access Journals (Sweden)
Dominik M Endres
2013-12-01
Full Text Available A wide range of blind source separation methods have been used in motor control research for the extraction of movement primitives from EMG and kinematic data. Popular examples are principal component analysis (PCA,independent component analysis (ICA, anechoic demixing, and the time-varying synergy model. However, choosing the parameters of these models, or indeed choosing the type of model, is often done in a heuristic fashion, driven by result expectations as much as by the data. We propose an objective criterion which allows to select the model type, number of primitives and the temporal smoothness prior. Our approach is based on a Laplace approximation to the posterior distribution of the parameters of a given blind source separation model, re-formulated as a Bayesian generative model.We first validate our criterion on ground truth data, showing that it performs at least as good as traditional model selection criteria (Bayesian information criterion, BIC and the Akaike Information Criterion (AIC. Then, we analyze human gait data, finding that an anechoic mixture model with a temporal smoothness constraint on the sources can best account for the data.
On selection of optimal stochastic model for accelerated life testing
International Nuclear Information System (INIS)
Volf, P.; Timková, J.
2014-01-01
This paper deals with the problem of proper lifetime model selection in the context of statistical reliability analysis. Namely, we consider regression models describing the dependence of failure intensities on a covariate, for instance, a stressor. Testing the model fit is standardly based on the so-called martingale residuals. Their analysis has already been studied by many authors. Nevertheless, the Bayes approach to the problem, in spite of its advantages, is just developing. We shall present the Bayes procedure of estimation in several semi-parametric regression models of failure intensity. Then, our main concern is the Bayes construction of residual processes and goodness-of-fit tests based on them. The method is illustrated with both artificial and real-data examples. - Highlights: • Statistical survival and reliability analysis and Bayes approach. • Bayes semi-parametric regression modeling in Cox's and AFT models. • Bayes version of martingale residuals and goodness-of-fit test
Model building strategy for logistic regression: purposeful selection.
Zhang, Zhongheng
2016-03-01
Logistic regression is one of the most commonly used models to account for confounders in medical literature. The article introduces how to perform purposeful selection model building strategy with R. I stress on the use of likelihood ratio test to see whether deleting a variable will have significant impact on model fit. A deleted variable should also be checked for whether it is an important adjustment of remaining covariates. Interaction should be checked to disentangle complex relationship between covariates and their synergistic effect on response variable. Model should be checked for the goodness-of-fit (GOF). In other words, how the fitted model reflects the real data. Hosmer-Lemeshow GOF test is the most widely used for logistic regression model.
Statistical modelling in biostatistics and bioinformatics selected papers
Peng, Defen
2014-01-01
This book presents selected papers on statistical model development related mainly to the fields of Biostatistics and Bioinformatics. The coverage of the material falls squarely into the following categories: (a) Survival analysis and multivariate survival analysis, (b) Time series and longitudinal data analysis, (c) Statistical model development and (d) Applied statistical modelling. Innovations in statistical modelling are presented throughout each of the four areas, with some intriguing new ideas on hierarchical generalized non-linear models and on frailty models with structural dispersion, just to mention two examples. The contributors include distinguished international statisticians such as Philip Hougaard, John Hinde, Il Do Ha, Roger Payne and Alessandra Durio, among others, as well as promising newcomers. Some of the contributions have come from researchers working in the BIO-SI research programme on Biostatistics and Bioinformatics, centred on the Universities of Limerick and Galway in Ireland and fu...
Bayesian Variable Selection on Model Spaces Constrained by Heredity Conditions.
Taylor-Rodriguez, Daniel; Womack, Andrew; Bliznyuk, Nikolay
2016-01-01
This paper investigates Bayesian variable selection when there is a hierarchical dependence structure on the inclusion of predictors in the model. In particular, we study the type of dependence found in polynomial response surfaces of orders two and higher, whose model spaces are required to satisfy weak or strong heredity conditions. These conditions restrict the inclusion of higher-order terms depending upon the inclusion of lower-order parent terms. We develop classes of priors on the model space, investigate their theoretical and finite sample properties, and provide a Metropolis-Hastings algorithm for searching the space of models. The tools proposed allow fast and thorough exploration of model spaces that account for hierarchical polynomial structure in the predictors and provide control of the inclusion of false positives in high posterior probability models.
Bakker, Eric
2010-02-15
A generalized description of the response behavior of potentiometric polymer membrane ion-selective electrodes is presented on the basis of ion-exchange equilibrium considerations at the sample-membrane interface. This paper includes and extends on previously reported theoretical advances in a more compact yet more comprehensive form. Specifically, the phase boundary potential model is used to derive the origin of the Nernstian response behavior in a single expression, which is valid for a membrane containing any charge type and complex stoichiometry of ionophore and ion-exchanger. This forms the basis for a generalized expression of the selectivity coefficient, which may be used for the selectivity optimization of ion-selective membranes containing electrically charged and neutral ionophores of any desired stoichiometry. It is shown to reduce to expressions published previously for specialized cases, and may be effectively applied to problems relevant in modern potentiometry. The treatment is extended to mixed ion solutions, offering a comprehensive yet formally compact derivation of the response behavior of ion-selective electrodes to a mixture of ions of any desired charge. It is compared to predictions by the less accurate Nicolsky-Eisenman equation. The influence of ion fluxes or any form of electrochemical excitation is not considered here, but may be readily incorporated if an ion-exchange equilibrium at the interface may be assumed in these cases.
A model for the sustainable selection of building envelope assemblies
International Nuclear Information System (INIS)
Huedo, Patricia; Mulet, Elena; López-Mesa, Belinda
2016-01-01
The aim of this article is to define an evaluation model for the environmental impacts of building envelopes to support planners in the early phases of materials selection. The model is intended to estimate environmental impacts for different combinations of building envelope assemblies based on scientifically recognised sustainability indicators. These indicators will increase the amount of information that existing catalogues show to support planners in the selection of building assemblies. To define the model, first the environmental indicators were selected based on the specific aims of the intended sustainability assessment. Then, a simplified LCA methodology was developed to estimate the impacts applicable to three types of dwellings considering different envelope assemblies, building orientations and climate zones. This methodology takes into account the manufacturing, installation, maintenance and use phases of the building. Finally, the model was validated and a matrix in Excel was created as implementation of the model. - Highlights: • Method to assess the envelope impacts based on a simplified LCA • To be used at an earlier phase than the existing methods in a simple way. • It assigns a score by means of known sustainability indicators. • It estimates data about the embodied and operating environmental impacts. • It compares the investment costs with the costs of the consumed energy.
PROPOSAL OF AN EMPIRICAL MODEL FOR SUPPLIERS SELECTION
Directory of Open Access Journals (Sweden)
Paulo Ávila
2015-03-01
Full Text Available The problem of selecting suppliers/partners is a crucial and important part in the process of decision making for companies that intend to perform competitively in their area of activity. The selection of supplier/partner is a time and resource-consuming task that involves data collection and a careful analysis of the factors that can positively or negatively influence the choice. Nevertheless it is a critical process that affects significantly the operational performance of each company. In this work, trough the literature review, there were identified five broad suppliers selection criteria: Quality, Financial, Synergies, Cost, and Production System. Within these criteria, it was also included five sub-criteria. Thereafter, a survey was elaborated and companies were contacted in order to answer which factors have more relevance in their decisions to choose the suppliers. Interpreted the results and processed the data, it was adopted a model of linear weighting to reflect the importance of each factor. The model has a hierarchical structure and can be applied with the Analytic Hierarchy Process (AHP method or Simple Multi-Attribute Rating Technique (SMART. The result of the research undertaken by the authors is a reference model that represents a decision making support for the suppliers/partners selection process.
Directory of Open Access Journals (Sweden)
Henry de-Graft Acquah
2013-01-01
Full Text Available Information Criteria provides an attractive basis for selecting the best model from a set of competing asymmetric price transmission models or theories. However, little is understood about the sensitivity of the model selection methods to model complexity. This study therefore fits competing asymmetric price transmission models that differ in complexity to simulated data and evaluates the ability of the model selection methods to recover the true model. The results of Monte Carlo experimentation suggest that in general BIC, CAIC and DIC were superior to AIC when the true data generating process was the standard error correction model, whereas AIC was more successful when the true model was the complex error correction model. It is also shown that the model selection methods performed better in large samples for a complex asymmetric data generating process than with a standard asymmetric data generating process. Except for complex models, AIC's performance did not make substantial gains in recovery rates as sample size increased. The research findings demonstrate the influence of model complexity in asymmetric price transmission model comparison and selection.
Broken selection rule in the quantum Rabi model.
Forn-Díaz, P; Romero, G; Harmans, C J P M; Solano, E; Mooij, J E
2016-06-07
Understanding the interaction between light and matter is very relevant for fundamental studies of quantum electrodynamics and for the development of quantum technologies. The quantum Rabi model captures the physics of a single atom interacting with a single photon at all regimes of coupling strength. We report the spectroscopic observation of a resonant transition that breaks a selection rule in the quantum Rabi model, implemented using an LC resonator and an artificial atom, a superconducting qubit. The eigenstates of the system consist of a superposition of bare qubit-resonator states with a relative sign. When the qubit-resonator coupling strength is negligible compared to their own frequencies, the matrix element between excited eigenstates of different sign is very small in presence of a resonator drive, establishing a sign-preserving selection rule. Here, our qubit-resonator system operates in the ultrastrong coupling regime, where the coupling strength is 10% of the resonator frequency, allowing sign-changing transitions to be activated and, therefore, detected. This work shows that sign-changing transitions are an unambiguous, distinctive signature of systems operating in the ultrastrong coupling regime of the quantum Rabi model. These results pave the way to further studies of sign-preserving selection rules in multiqubit and multiphoton models.
Models of cultural niche construction with selection and assortative mating.
Creanza, Nicole; Fogarty, Laurel; Feldman, Marcus W
2012-01-01
Niche construction is a process through which organisms modify their environment and, as a result, alter the selection pressures on themselves and other species. In cultural niche construction, one or more cultural traits can influence the evolution of other cultural or biological traits by affecting the social environment in which the latter traits may evolve. Cultural niche construction may include either gene-culture or culture-culture interactions. Here we develop a model of this process and suggest some applications of this model. We examine the interactions between cultural transmission, selection, and assorting, paying particular attention to the complexities that arise when selection and assorting are both present, in which case stable polymorphisms of all cultural phenotypes are possible. We compare our model to a recent model for the joint evolution of religion and fertility and discuss other potential applications of cultural niche construction theory, including the evolution and maintenance of large-scale human conflict and the relationship between sex ratio bias and marriage customs. The evolutionary framework we introduce begins to address complexities that arise in the quantitative analysis of multiple interacting cultural traits.
Models of cultural niche construction with selection and assortative mating.
Directory of Open Access Journals (Sweden)
Nicole Creanza
Full Text Available Niche construction is a process through which organisms modify their environment and, as a result, alter the selection pressures on themselves and other species. In cultural niche construction, one or more cultural traits can influence the evolution of other cultural or biological traits by affecting the social environment in which the latter traits may evolve. Cultural niche construction may include either gene-culture or culture-culture interactions. Here we develop a model of this process and suggest some applications of this model. We examine the interactions between cultural transmission, selection, and assorting, paying particular attention to the complexities that arise when selection and assorting are both present, in which case stable polymorphisms of all cultural phenotypes are possible. We compare our model to a recent model for the joint evolution of religion and fertility and discuss other potential applications of cultural niche construction theory, including the evolution and maintenance of large-scale human conflict and the relationship between sex ratio bias and marriage customs. The evolutionary framework we introduce begins to address complexities that arise in the quantitative analysis of multiple interacting cultural traits.
Selection of Models for Ingestion Pathway and Relocation Radii Determination
International Nuclear Information System (INIS)
Blanchard, A.
1998-01-01
The distance at which intermediate phase protective actions (such as food interdiction and relocation) may be needed following postulated accidents at three Savannah River Site nonreactor nuclear facilities will be determined by modeling. The criteria used to select dispersion/deposition models are presented. Several models were considered, including ARAC, MACCS, HOTSPOT, WINDS (coupled with PUFF-PLUME), and UFOTRI. Although ARAC and WINDS are expected to provide more accurate modeling of atmospheric transport following an actual release, analyses consistent with regulatory guidance for planning purposes may be accomplished with comparatively simple dispersion models such as HOTSPOT and UFOTRI. A recommendation is made to use HOTSPOT for non-tritium facilities and UFOTRI for tritium facilities
Numerical Model based Reliability Estimation of Selective Laser Melting Process
DEFF Research Database (Denmark)
Mohanty, Sankhya; Hattel, Jesper Henri
2014-01-01
Selective laser melting is developing into a standard manufacturing technology with applications in various sectors. However, the process is still far from being at par with conventional processes such as welding and casting, the primary reason of which is the unreliability of the process. While...... of the selective laser melting process. A validated 3D finite-volume alternating-direction-implicit numerical technique is used to model the selective laser melting process, and is calibrated against results from single track formation experiments. Correlation coefficients are determined for process input...... parameters such as laser power, speed, beam profile, etc. Subsequently, uncertainties in the processing parameters are utilized to predict a range for the various outputs, using a Monte Carlo method based uncertainty analysis methodology, and the reliability of the process is established....
Modelling Technical and Economic Parameters in Selection of Manufacturing Devices
Directory of Open Access Journals (Sweden)
Naqib Daneshjo
2017-11-01
Full Text Available Sustainable science and technology development is also conditioned by continuous development of means of production which have a key role in structure of each production system. Mechanical nature of the means of production is complemented by controlling and electronic devices in context of intelligent industry. A selection of production machines for a technological process or technological project has so far been practically resolved, often only intuitively. With regard to increasing intelligence, the number of variable parameters that have to be considered when choosing a production device is also increasing. It is necessary to use computing techniques and decision making methods according to heuristic methods and more precise methodological procedures during the selection. The authors present an innovative model for optimization of technical and economic parameters in the selection of manufacturing devices for industry 4.0.
Forecasting house prices in the 50 states using Dynamic Model Averaging and Dynamic Model Selection
DEFF Research Database (Denmark)
Bork, Lasse; Møller, Stig Vinther
2015-01-01
We examine house price forecastability across the 50 states using Dynamic Model Averaging and Dynamic Model Selection, which allow for model change and parameter shifts. By allowing the entire forecasting model to change over time and across locations, the forecasting accuracy improves substantia...
Quantitative modeling of selective lysosomal targeting for drug design
DEFF Research Database (Denmark)
Trapp, Stefan; Rosania, G.; Horobin, R.W.
2008-01-01
Lysosomes are acidic organelles and are involved in various diseases, the most prominent is malaria. Accumulation of molecules in the cell by diffusion from the external solution into cytosol, lysosome and mitochondrium was calculated with the Fick–Nernst–Planck equation. The cell model considers...... the diffusion of neutral and ionic molecules across biomembranes, protonation to mono- or bivalent ions, adsorption to lipids, and electrical attraction or repulsion. Based on simulation results, high and selective accumulation in lysosomes was found for weak mono- and bivalent bases with intermediate to high...... predicted by the model and three were close. Five of the antimalarial drugs were lipophilic weak dibasic compounds. The predicted optimum properties for a selective accumulation of weak bivalent bases in lysosomes are consistent with experimental values and are more accurate than any prior calculation...
Genomic Selection in Plant Breeding: Methods, Models, and Perspectives.
Crossa, José; Pérez-Rodríguez, Paulino; Cuevas, Jaime; Montesinos-López, Osval; Jarquín, Diego; de Los Campos, Gustavo; Burgueño, Juan; González-Camacho, Juan M; Pérez-Elizalde, Sergio; Beyene, Yoseph; Dreisigacker, Susanne; Singh, Ravi; Zhang, Xuecai; Gowda, Manje; Roorkiwal, Manish; Rutkoski, Jessica; Varshney, Rajeev K
2017-11-01
Genomic selection (GS) facilitates the rapid selection of superior genotypes and accelerates the breeding cycle. In this review, we discuss the history, principles, and basis of GS and genomic-enabled prediction (GP) as well as the genetics and statistical complexities of GP models, including genomic genotype×environment (G×E) interactions. We also examine the accuracy of GP models and methods for two cereal crops and two legume crops based on random cross-validation. GS applied to maize breeding has shown tangible genetic gains. Based on GP results, we speculate how GS in germplasm enhancement (i.e., prebreeding) programs could accelerate the flow of genes from gene bank accessions to elite lines. Recent advances in hyperspectral image technology could be combined with GS and pedigree-assisted breeding. Copyright © 2017 Elsevier Ltd. All rights reserved.
Generalized Degrees of Freedom and Adaptive Model Selection in Linear Mixed-Effects Models.
Zhang, Bo; Shen, Xiaotong; Mumford, Sunni L
2012-03-01
Linear mixed-effects models involve fixed effects, random effects and covariance structure, which require model selection to simplify a model and to enhance its interpretability and predictability. In this article, we develop, in the context of linear mixed-effects models, the generalized degrees of freedom and an adaptive model selection procedure defined by a data-driven model complexity penalty. Numerically, the procedure performs well against its competitors not only in selecting fixed effects but in selecting random effects and covariance structure as well. Theoretically, asymptotic optimality of the proposed methodology is established over a class of information criteria. The proposed methodology is applied to the BioCycle study, to determine predictors of hormone levels among premenopausal women and to assess variation in hormone levels both between and within women across the menstrual cycle.
Parameter estimation and model selection in computational biology.
Directory of Open Access Journals (Sweden)
Gabriele Lillacci
2010-03-01
Full Text Available A central challenge in computational modeling of biological systems is the determination of the model parameters. Typically, only a fraction of the parameters (such as kinetic rate constants are experimentally measured, while the rest are often fitted. The fitting process is usually based on experimental time course measurements of observables, which are used to assign parameter values that minimize some measure of the error between these measurements and the corresponding model prediction. The measurements, which can come from immunoblotting assays, fluorescent markers, etc., tend to be very noisy and taken at a limited number of time points. In this work we present a new approach to the problem of parameter selection of biological models. We show how one can use a dynamic recursive estimator, known as extended Kalman filter, to arrive at estimates of the model parameters. The proposed method follows. First, we use a variation of the Kalman filter that is particularly well suited to biological applications to obtain a first guess for the unknown parameters. Secondly, we employ an a posteriori identifiability test to check the reliability of the estimates. Finally, we solve an optimization problem to refine the first guess in case it should not be accurate enough. The final estimates are guaranteed to be statistically consistent with the measurements. Furthermore, we show how the same tools can be used to discriminate among alternate models of the same biological process. We demonstrate these ideas by applying our methods to two examples, namely a model of the heat shock response in E. coli, and a model of a synthetic gene regulation system. The methods presented are quite general and may be applied to a wide class of biological systems where noisy measurements are used for parameter estimation or model selection.
Pareto-Optimal Model Selection via SPRINT-Race.
Zhang, Tiantian; Georgiopoulos, Michael; Anagnostopoulos, Georgios C
2018-02-01
In machine learning, the notion of multi-objective model selection (MOMS) refers to the problem of identifying the set of Pareto-optimal models that optimize by compromising more than one predefined objectives simultaneously. This paper introduces SPRINT-Race, the first multi-objective racing algorithm in a fixed-confidence setting, which is based on the sequential probability ratio with indifference zone test. SPRINT-Race addresses the problem of MOMS with multiple stochastic optimization objectives in the proper Pareto-optimality sense. In SPRINT-Race, a pairwise dominance or non-dominance relationship is statistically inferred via a non-parametric, ternary-decision, dual-sequential probability ratio test. The overall probability of falsely eliminating any Pareto-optimal models or mistakenly returning any clearly dominated models is strictly controlled by a sequential Holm's step-down family-wise error rate control method. As a fixed-confidence model selection algorithm, the objective of SPRINT-Race is to minimize the computational effort required to achieve a prescribed confidence level about the quality of the returned models. The performance of SPRINT-Race is first examined via an artificially constructed MOMS problem with known ground truth. Subsequently, SPRINT-Race is applied on two real-world applications: 1) hybrid recommender system design and 2) multi-criteria stock selection. The experimental results verify that SPRINT-Race is an effective and efficient tool for such MOMS problems. code of SPRINT-Race is available at https://github.com/watera427/SPRINT-Race.
Models of speciation by sexual selection on polygenic traits
Lande, Russell
1981-01-01
The joint evolution of female mating preferences and secondary sexual characters of males is modeled for polygamous species in which males provide only genetic material to the next generation and females have many potential mates to choose among. Despite stabilizing natural selection on males, various types of mating preferences may create a runaway process in which the outcome of phenotypic evolution depends critically on the genetic variation parameters and initial conditions of a populatio...
A Model of Social Selection and Successful Altruism
1989-10-07
D., The evolution of social behavior. Annual Reviews of Ecological Systems, 5:325-383 (1974). 2. Dawkins , R., The selfish gene . Oxford: Oxford...alive and well. it will be important to re- examine this striking historical experience,-not in terms o, oversimplified models of the " selfish gene ," but...Darwinian Analysis The acceptance by many modern geneticists of the axiom that the basic unit of selection Is the " selfish gene " quickly led to the
A Bayesian Technique for Selecting a Linear Forecasting Model
Ramona L. Trader
1983-01-01
The specification of a forecasting model is considered in the context of linear multiple regression. Several potential predictor variables are available, but some of them convey little information about the dependent variable which is to be predicted. A technique for selecting the "best" set of predictors which takes into account the inherent uncertainty in prediction is detailed. In addition to current data, there is often substantial expert opinion available which is relevant to the forecas...
Directory of Open Access Journals (Sweden)
Ser Javier Del
2005-01-01
Full Text Available We consider the case of two correlated sources, S 1 and S 2 . The correlation between them has memory, and it is modelled by a hidden Markov chain. The paper studies the problem of reliable communication of the information sent by the source S 1 over an additive white Gaussian noise (AWGN channel when the output of the other source S 2 is available as side information at the receiver. We assume that the receiver has no a priori knowledge of the correlation statistics between the sources. In particular, we propose the use of a turbo code for joint source-channel coding of the source S 1 . The joint decoder uses an iterative scheme where the unknown parameters of the correlation model are estimated jointly within the decoding process. It is shown that reliable communication is possible at signal-to-noise ratios close to the theoretical limits set by the combination of Shannon and Slepian-Wolf theorems.
Directory of Open Access Journals (Sweden)
Ser Javier Del
2005-01-01
Full Text Available We consider the case of two correlated sources, and . The correlation between them has memory, and it is modelled by a hidden Markov chain. The paper studies the problem of reliable communication of the information sent by the source over an additive white Gaussian noise (AWGN channel when the output of the other source is available as side information at the receiver. We assume that the receiver has no a priori knowledge of the correlation statistics between the sources. In particular, we propose the use of a turbo code for joint source-channel coding of the source . The joint decoder uses an iterative scheme where the unknown parameters of the correlation model are estimated jointly within the decoding process. It is shown that reliable communication is possible at signal-to-noise ratios close to the theoretical limits set by the combination of Shannon and Slepian-Wolf theorems.
Multi-stream LSTM-HMM decoding and histogram equalization for noise robust keyword spotting.
Wöllmer, Martin; Marchi, Erik; Squartini, Stefano; Schuller, Björn
2011-09-01
Highly spontaneous, conversational, and potentially emotional and noisy speech is known to be a challenge for today's automatic speech recognition (ASR) systems, which highlights the need for advanced algorithms that improve speech features and models. Histogram Equalization is an efficient method to reduce the mismatch between clean and noisy conditions by normalizing all moments of the probability distribution of the feature vector components. In this article, we propose to combine histogram equalization and multi-condition training for robust keyword detection in noisy speech. To better cope with conversational speaking styles, we show how contextual information can be effectively exploited in a multi-stream ASR framework that dynamically models context-sensitive phoneme estimates generated by a long short-term memory neural network. The proposed techniques are evaluated on the SEMAINE database-a corpus containing emotionally colored conversations with a cognitive system for "Sensitive Artificial Listening".
histoneHMM: Differential analysis of histone modifications with broad genomic footprints
Czech Academy of Sciences Publication Activity Database
Heinig, M.; Colomé-Tatché, M.; Taudt, A.; Rintisch, C.; Schafer, S.; Pravenec, Michal; Hubner, N.; Vingron, M.; Johannes, F.
2015-01-01
Roč. 16, Feb 22 (2015), s. 60 ISSN 1471-2105 R&D Projects: GA MŠk(CZ) 7E10067; GA ČR(CZ) GA13-04420S Institutional support: RVO:67985823 Keywords : ChIP - seq * histone modifications * Hidden Markov model * computational biology * differential analysis Subject RIV: EB - Genetics ; Molecular Biology Impact factor: 2.435, year: 2015
Rafii-Tari, Hedyeh; Liu, Jindong; Payne, Christopher J; Bicknell, Colin; Yang, Guang-Zhong
2014-01-01
Despite increased use of remote-controlled steerable catheter navigation systems for endovascular intervention, most current designs are based on master configurations which tend to alter natural operator tool interactions. This introduces problems to both ergonomics and shared human-robot control. This paper proposes a novel cooperative robotic catheterization system based on learning-from-demonstration. By encoding the higher-level structure of a catheterization task as a sequence of primitive motions, we demonstrate how to achieve prospective learning for complex tasks whilst incorporating subject-specific variations. A hierarchical Hidden Markov Model is used to model each movement primitive as well as their sequential relationship. This model is applied to generation of motion sequences, recognition of operator input, and prediction of future movements for the robot. The framework is validated by comparing catheter tip motions against the manual approach, showing significant improvements in the quality of catheterization. The results motivate the design of collaborative robotic systems that are intuitive to use, while reducing the cognitive workload of the operator.
A decision model for energy resource selection in China
International Nuclear Information System (INIS)
Wang Bing; Kocaoglu, Dundar F.; Daim, Tugrul U.; Yang Jiting
2010-01-01
This paper evaluates coal, petroleum, natural gas, nuclear energy and renewable energy resources as energy alternatives for China through use of a hierarchical decision model. The results indicate that although coal is still the major preferred energy alternative, it is followed closely by renewable energy. The sensitivity analysis indicates that the most critical criterion for energy selection is the current energy infrastructure. A hierarchical decision model is used, and expert judgments are quantified, to evaluate the alternatives. Criteria used for the evaluations are availability, current energy infrastructure, price, safety, environmental impacts and social impacts.
Selection of key terrain attributes for SOC model
DEFF Research Database (Denmark)
Greve, Mogens Humlekrog; Adhikari, Kabindra; Chellasamy, Menaka
As an important component of the global carbon pool, soil organic carbon (SOC) plays an important role in the global carbon cycle. SOC pool is the basic information to carry out global warming research, and needs to sustainable use of land resources. Digital terrain attributes are often use...... was selected, total 2,514,820 data mining models were constructed by 71 differences grid from 12m to 2304m and 22 attributes, 21 attributes derived by DTM and the original elevation. Relative importance and usage of each attributes in every model were calculated. Comprehensive impact rates of each attribute...
Sharma, Ronesh; Bayarjargal, Maitsetseg; Tsunoda, Tatsuhiko; Patil, Ashwini; Sharma, Alok
2018-01-21
Intrinsically Disordered Proteins (IDPs) lack stable tertiary structure and they actively participate in performing various biological functions. These IDPs expose short binding regions called Molecular Recognition Features (MoRFs) that permit interaction with structured protein regions. Upon interaction they undergo a disorder-to-order transition as a result of which their functionality arises. Predicting these MoRFs in disordered protein sequences is a challenging task. In this study, we present MoRFpred-plus, an improved predictor over our previous proposed predictor to identify MoRFs in disordered protein sequences. Two separate independent propensity scores are computed via incorporating physicochemical properties and HMM profiles, these scores are combined to predict final MoRF propensity score for a given residue. The first score reflects the characteristics of a query residue to be part of MoRF region based on the composition and similarity of assumed MoRF and flank regions. The second score reflects the characteristics of a query residue to be part of MoRF region based on the properties of flanks associated around the given residue in the query protein sequence. The propensity scores are processed and common averaging is applied to generate the final prediction score of MoRFpred-plus. Performance of the proposed predictor is compared with available MoRF predictors, MoRFchibi, MoRFpred, and ANCHOR. Using previously collected training and test sets used to evaluate the mentioned predictors, the proposed predictor outperforms these predictors and generates lower false positive rate. In addition, MoRFpred-plus is a downloadable predictor, which makes it useful as it can be used as input to other computational tools. https://github.com/roneshsharma/MoRFpred-plus/wiki/MoRFpred-plus:-Download. Copyright © 2017 Elsevier Ltd. All rights reserved.
Selecting, weeding, and weighting biased climate model ensembles
Jackson, C. S.; Picton, J.; Huerta, G.; Nosedal Sanchez, A.
2012-12-01
In the Bayesian formulation, the "log-likelihood" is a test statistic for selecting, weeding, or weighting climate model ensembles with observational data. This statistic has the potential to synthesize the physical and data constraints on quantities of interest. One of the thorny issues for formulating the log-likelihood is how one should account for biases. While in the past we have included a generic discrepancy term, not all biases affect predictions of quantities of interest. We make use of a 165-member ensemble CAM3.1/slab ocean climate models with different parameter settings to think through the issues that are involved with predicting each model's sensitivity to greenhouse gas forcing given what can be observed from the base state. In particular we use multivariate empirical orthogonal functions to decompose the differences that exist among this ensemble to discover what fields and regions matter to the model's sensitivity. We find that the differences that matter are a small fraction of the total discrepancy. Moreover, weighting members of the ensemble using this knowledge does a relatively poor job of adjusting the ensemble mean toward the known answer. This points out the shortcomings of using weights to correct for biases in climate model ensembles created by a selection process that does not emphasize the priorities of your log-likelihood.
Optimal foraging in marine ecosystem models: selectivity, profitability and switching
DEFF Research Database (Denmark)
Visser, Andre W.; Fiksen, Ø.
2013-01-01
ecological mechanics and evolutionary logic as a solution to diet selection in ecosystem models. When a predator can consume a range of prey items it has to choose which foraging mode to use, which prey to ignore and which ones to pursue, and animals are known to be particularly skilled in adapting...... to the preference functions commonly used in models today. Indeed, depending on prey class resolution, optimal foraging can yield feeding rates that are considerably different from the ‘switching functions’ often applied in marine ecosystem models. Dietary inclusion is dictated by two optimality choices: 1...... by letting predators maximize energy intake or more properly, some measure of fitness where predation risk and cost are also included. An optimal foraging or fitness maximizing approach will give marine ecosystem models a sound principle to determine trophic interactions...
Covariate selection for the semiparametric additive risk model
DEFF Research Database (Denmark)
Martinussen, Torben; Scheike, Thomas
2009-01-01
This paper considers covariate selection for the additive hazards model. This model is particularly simple to study theoretically and its practical implementation has several major advantages to the similar methodology for the proportional hazards model. One complication compared...... and study their large sample properties for the situation where the number of covariates p is smaller than the number of observations. We also show that the adaptive Lasso has the oracle property. In many practical situations, it is more relevant to tackle the situation with large p compared with the number...... of observations. We do this by studying the properties of the so-called Dantzig selector in the setting of the additive risk model. Specifically, we establish a bound on how close the solution is to a true sparse signal in the case where the number of covariates is large. In a simulation study, we also compare...
Selection of productivity improvement techniques via mathematical modeling
Directory of Open Access Journals (Sweden)
Mahassan M. Khater
2011-07-01
Full Text Available This paper presents a new mathematical model to select an optimal combination of productivity improvement techniques. The proposed model of this paper considers four-stage cycle productivity and the productivity is assumed to be a linear function of fifty four improvement techniques. The proposed model of this paper is implemented for a real-world case study of manufacturing plant. The resulted problem is formulated as a mixed integer programming which can be solved for optimality using traditional methods. The preliminary results of the implementation of the proposed model of this paper indicate that the productivity can be improved through a change on equipments and it can be easily applied for both manufacturing and service industries.
An Introduction to Model Selection: Tools and Algorithms
Directory of Open Access Journals (Sweden)
Sébastien Hélie
2006-03-01
Full Text Available Model selection is a complicated matter in science, and psychology is no exception. In particular, the high variance in the object of study (i.e., humans prevents the use of Poppers falsification principle (which is the norm in other sciences. Therefore, the desirability of quantitative psychological models must be assessed by measuring the capacity of the model to fit empirical data. In the present paper, an error measure (likelihood, as well as five methods to compare model fits (the likelihood ratio test, Akaikes information criterion, the Bayesian information criterion, bootstrapping and cross-validation, are presented. The use of each method is illustrated by an example, and the advantages and weaknesses of each method are also discussed.
González, Alvaro J; Liao, Li
2010-10-29
Protein-protein interaction (PPI) plays essential roles in cellular functions. The cost, time and other limitations associated with the current experimental methods have motivated the development of computational methods for predicting PPIs. As protein interactions generally occur via domains instead of the whole molecules, predicting domain-domain interaction (DDI) is an important step toward PPI prediction. Computational methods developed so far have utilized information from various sources at different levels, from primary sequences, to molecular structures, to evolutionary profiles. In this paper, we propose a computational method to predict DDI using support vector machines (SVMs), based on domains represented as interaction profile hidden Markov models (ipHMM) where interacting residues in domains are explicitly modeled according to the three dimensional structural information available at the Protein Data Bank (PDB). Features about the domains are extracted first as the Fisher scores derived from the ipHMM and then selected using singular value decomposition (SVD). Domain pairs are represented by concatenating their selected feature vectors, and classified by a support vector machine trained on these feature vectors. The method is tested by leave-one-out cross validation experiments with a set of interacting protein pairs adopted from the 3DID database. The prediction accuracy has shown significant improvement as compared to InterPreTS (Interaction Prediction through Tertiary Structure), an existing method for PPI prediction that also uses the sequences and complexes of known 3D structure. We show that domain-domain interaction prediction can be significantly enhanced by exploiting information inherent in the domain profiles via feature selection based on Fisher scores, singular value decomposition and supervised learning based on support vector machines. Datasets and source code are freely available on the web at http
Directory of Open Access Journals (Sweden)
Liao Li
2010-10-01
Full Text Available Abstract Background Protein-protein interaction (PPI plays essential roles in cellular functions. The cost, time and other limitations associated with the current experimental methods have motivated the development of computational methods for predicting PPIs. As protein interactions generally occur via domains instead of the whole molecules, predicting domain-domain interaction (DDI is an important step toward PPI prediction. Computational methods developed so far have utilized information from various sources at different levels, from primary sequences, to molecular structures, to evolutionary profiles. Results In this paper, we propose a computational method to predict DDI using support vector machines (SVMs, based on domains represented as interaction profile hidden Markov models (ipHMM where interacting residues in domains are explicitly modeled according to the three dimensional structural information available at the Protein Data Bank (PDB. Features about the domains are extracted first as the Fisher scores derived from the ipHMM and then selected using singular value decomposition (SVD. Domain pairs are represented by concatenating their selected feature vectors, and classified by a support vector machine trained on these feature vectors. The method is tested by leave-one-out cross validation experiments with a set of interacting protein pairs adopted from the 3DID database. The prediction accuracy has shown significant improvement as compared to InterPreTS (Interaction Prediction through Tertiary Structure, an existing method for PPI prediction that also uses the sequences and complexes of known 3D structure. Conclusions We show that domain-domain interaction prediction can be significantly enhanced by exploiting information inherent in the domain profiles via feature selection based on Fisher scores, singular value decomposition and supervised learning based on support vector machines. Datasets and source code are freely available on
A Semi-Continuous State-Transition Probability HMM-Based Voice Activity Detector
Directory of Open Access Journals (Sweden)
H. Othman
2007-02-01
Full Text Available We introduce an efficient hidden Markov model-based voice activity detection (VAD algorithm with time-variant state-transition probabilities in the underlying Markov chain. The transition probabilities vary in an exponential charge/discharge scheme and are softly merged with state conditional likelihood into a final VAD decision. Working in the domain of ITU-T G.729 parameters, with no additional cost for feature extraction, the proposed algorithm significantly outperforms G.729 Annex B VAD while providing a balanced tradeoff between clipping and false detection errors. The performance compares very favorably with the adaptive multirate VAD, option 2 (AMR2.
A Semi-Continuous State-Transition Probability HMM-Based Voice Activity Detector
Directory of Open Access Journals (Sweden)
Othman H
2007-01-01
Full Text Available We introduce an efficient hidden Markov model-based voice activity detection (VAD algorithm with time-variant state-transition probabilities in the underlying Markov chain. The transition probabilities vary in an exponential charge/discharge scheme and are softly merged with state conditional likelihood into a final VAD decision. Working in the domain of ITU-T G.729 parameters, with no additional cost for feature extraction, the proposed algorithm significantly outperforms G.729 Annex B VAD while providing a balanced tradeoff between clipping and false detection errors. The performance compares very favorably with the adaptive multirate VAD, option 2 (AMR2.
Selection of Representative Models for Decision Analysis Under Uncertainty
Meira, Luis A. A.; Coelho, Guilherme P.; Santos, Antonio Alberto S.; Schiozer, Denis J.
2016-03-01
The decision-making process in oil fields includes a step of risk analysis associated with the uncertainties present in the variables of the problem. Such uncertainties lead to hundreds, even thousands, of possible scenarios that are supposed to be analyzed so an effective production strategy can be selected. Given this high number of scenarios, a technique to reduce this set to a smaller, feasible subset of representative scenarios is imperative. The selected scenarios must be representative of the original set and also free of optimistic and pessimistic bias. This paper is devoted to propose an assisted methodology to identify representative models in oil fields. To do so, first a mathematical function was developed to model the representativeness of a subset of models with respect to the full set that characterizes the problem. Then, an optimization tool was implemented to identify the representative models of any problem, considering not only the cross-plots of the main output variables, but also the risk curves and the probability distribution of the attribute-levels of the problem. The proposed technique was applied to two benchmark cases and the results, evaluated by experts in the field, indicate that the obtained solutions are richer than those identified by previously adopted manual approaches. The program bytecode is available under request.
Selecting global climate models for regional climate change studies.
Pierce, David W; Barnett, Tim P; Santer, Benjamin D; Gleckler, Peter J
2009-05-26
Regional or local climate change modeling studies currently require starting with a global climate model, then downscaling to the region of interest. How should global models be chosen for such studies, and what effect do such choices have? This question is addressed in the context of a regional climate detection and attribution (D&A) study of January-February-March (JFM) temperature over the western U.S. Models are often selected for a regional D&A analysis based on the quality of the simulated regional climate. Accordingly, 42 performance metrics based on seasonal temperature and precipitation, the El Nino/Southern Oscillation (ENSO), and the Pacific Decadal Oscillation are constructed and applied to 21 global models. However, no strong relationship is found between the score of the models on the metrics and results of the D&A analysis. Instead, the importance of having ensembles of runs with enough realizations to reduce the effects of natural internal climate variability is emphasized. Also, the superiority of the multimodel ensemble average (MM) to any 1 individual model, already found in global studies examining the mean climate, is true in this regional study that includes measures of variability as well. Evidence is shown that this superiority is largely caused by the cancellation of offsetting errors in the individual global models. Results with both the MM and models picked randomly confirm the original D&A results of anthropogenically forced JFM temperature changes in the western U.S. Future projections of temperature do not depend on model performance until the 2080s, after which the better performing models show warmer temperatures.
Selecting an Appropriate Upscaled Reservoir Model Based on Connectivity Analysis
Directory of Open Access Journals (Sweden)
Preux Christophe
2016-09-01
Full Text Available Reservoir engineers aim to build reservoir models to investigate fluid flows within hydrocarbon reservoirs. These models consist of three-dimensional grids populated by petrophysical properties. In this paper, we focus on permeability that is known to significantly influence fluid flow. Reservoir models usually encompass a very large number of fine grid blocks to better represent heterogeneities. However, performing fluid flow simulations for such fine models is extensively CPU-time consuming. A common practice consists in converting the fine models into coarse models with less grid blocks: this is the upscaling process. Many upscaling methods have been proposed in the literature that all lead to distinct coarse models. The problem is how to choose the appropriate upscaling method. Various criteria have been established to evaluate the information loss due to upscaling, but none of them investigate connectivity. In this paper, we propose to first perform a connectivity analysis for the fine and candidate coarse models. This makes it possible to identify shortest paths connecting wells. Then, we introduce two indicators to quantify the length and trajectory mismatch between the paths for the fine and the coarse models. The upscaling technique to be recommended is the one that provides the coarse model for which the shortest paths are the closest to the shortest paths determined for the fine model, both in terms of length and trajectory. Last, the potential of this methodology is investigated from two test cases. We show that the two indicators help select suitable upscaling techniques as long as gravity is not a prominent factor that drives fluid flows.
Bioeconomic model and selection indices in Aberdeen Angus cattle.
Campos, G S; Braccini Neto, J; Oaigen, R P; Cardoso, F F; Cobuci, J A; Kern, E L; Campos, L T; Bertoli, C D; McManus, C M
2014-08-01
A bioeconomic model was developed to calculate economic values for biological traits in full-cycle production systems and propose selection indices based on selection criteria used in the Brazilian Aberdeen Angus genetic breeding programme (PROMEBO). To assess the impact of changes in the performance of the traits on the profit of the production system, the initial values of the traits were increased by 1%. The economic values for number of calves weaned (NCW) and slaughter weight (SW) were, respectively, R$ 6.65 and R$ 1.43/cow/year. The selection index at weaning showed a 44.77% emphasis on body weight, 14.24% for conformation, 30.36% for early maturing and 10.63% for muscle development. The eighteen-month index showed emphasis of 77.61% for body weight, 4.99% for conformation, 11.09% for early maturing, 6.10% for muscle development and 0.22% for scrotal circumference. NCW showed highest economic impact, and SW had important positive effect on the economics of the production system. The selection index proposed can be used by breeders and should contribute to greater profitability. © 2014 Blackwell Verlag GmbH.
Improved hidden Markov model for nosocomial infections.
Khader, Karim; Leecaster, Molly; Greene, Tom; Samore, Matthew; Thomas, Alun
2014-12-01
We propose a novel hidden Markov model (HMM) for parameter estimation in hospital transmission models, and show that commonly made simplifying assumptions can lead to severe model misspecification and poor parameter estimates. A standard HMM that embodies two commonly made simplifying assumptions, namely a fixed patient count and binomially distributed detections is compared with a new alternative HMM that does not require these simplifying assumptions. Using simulated data, we demonstrate how each of the simplifying assumptions used by the standard model leads to model misspecification, whereas the alternative model results in accurate parameter estimates. © The Authors 2013. Published by Oxford University Press on behalf of the Institute of Mathematics and its Applications. All rights reserved.
A Self-Adaptive Hidden Markov Model for Emotion Classification in Chinese Microblogs
Directory of Open Access Journals (Sweden)
Li Liu
2015-01-01
we propose a modified version of hidden Markov model (HMM classifier, called self-adaptive HMM, whose parameters are optimized by Particle Swarm Optimization algorithms. Since manually labeling large-scale dataset is difficult, we also employ the entropy to decide whether a new unlabeled tweet shall be contained in the training dataset after being assigned an emotion using our HMM-based approach. In the experiment, we collected about 200,000 Chinese tweets from Sina Weibo. The results show that the F-score of our approach gets 76% on happiness and fear and 65% on anger, surprise, and sadness. In addition, the self-adaptive HMM classifier outperforms Naive Bayes and Support Vector Machine on recognition of happiness, anger, and sadness.
Improving the text classification using clustering and a novel HMM to reduce the dimensionality.
Seara Vieira, A; Borrajo, L; Iglesias, E L
2016-11-01
In text classification problems, the representation of a document has a strong impact on the performance of learning systems. The high dimensionality of the classical structured representations can lead to burdensome computations due to the great size of real-world data. Consequently, there is a need for reducing the quantity of handled information to improve the classification process. In this paper, we propose a method to reduce the dimensionality of a classical text representation based on a clustering technique to group documents, and a previously developed Hidden Markov Model to represent them. We have applied tests with the k-NN and SVM classifiers on the OHSUMED and TREC benchmark text corpora using the proposed dimensionality reduction technique. The experimental results obtained are very satisfactory compared to commonly used techniques like InfoGain and the statistical tests performed demonstrate the suitability of the proposed technique for the preprocessing step in a text classification task. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.
How Many Separable Sources? Model Selection In Independent Components Analysis
DEFF Research Database (Denmark)
Woods, Roger P.; Hansen, Lars Kai; Strother, Stephen
2015-01-01
Unlike mixtures consisting solely of non-Gaussian sources, mixtures including two or more Gaussian components cannot be separated using standard independent components analysis methods that are based on higher order statistics and independent observations. The mixed Independent Components Analysis....../Principal Components Analysis (mixed ICA/PCA) model described here accommodates one or more Gaussian components in the independent components analysis model and uses principal components analysis to characterize contributions from this inseparable Gaussian subspace. Information theory can then be used to select from...... among potential model categories with differing numbers of Gaussian components. Based on simulation studies, the assumptions and approximations underlying the Akaike Information Criterion do not hold in this setting, even with a very large number of observations. Cross-validation is a suitable, though...
Auditory-model based robust feature selection for speech recognition.
Koniaris, Christos; Kuropatwinski, Marcin; Kleijn, W Bastiaan
2010-02-01
It is shown that robust dimension-reduction of a feature set for speech recognition can be based on a model of the human auditory system. Whereas conventional methods optimize classification performance, the proposed method exploits knowledge implicit in the auditory periphery, inheriting its robustness. Features are selected to maximize the similarity of the Euclidean geometry of the feature domain and the perceptual domain. Recognition experiments using mel-frequency cepstral coefficients (MFCCs) confirm the effectiveness of the approach, which does not require labeled training data. For noisy data the method outperforms commonly used discriminant-analysis based dimension-reduction methods that rely on labeling. The results indicate that selecting MFCCs in their natural order results in subsets with good performance.
METHODS OF SELECTING THE EFFECTIVE MODELS OF BUILDINGS REPROFILING PROJECTS
Directory of Open Access Journals (Sweden)
Александр Иванович МЕНЕЙЛЮК
2016-02-01
Full Text Available The article highlights the important task of project management in reprofiling of buildings. It is expedient to pay attention to selecting effective engineering solutions to reduce the duration and cost reduction at the project management in the construction industry. This article presents a methodology for the selection of efficient organizational and technical solutions for the reconstruction of buildings reprofiling. The method is based on a compilation of project variants in the program Microsoft Project and experimental statistical analysis using the program COMPEX. The introduction of this technique in the realigning of buildings allows choosing efficient models of projects, depending on the given constraints. Also, this technique can be used for various construction projects.
Applying a Hybrid MCDM Model for Six Sigma Project Selection
Directory of Open Access Journals (Sweden)
Fu-Kwun Wang
2014-01-01
Full Text Available Six Sigma is a project-driven methodology; the projects that provide the maximum financial benefits and other impacts to the organization must be prioritized. Project selection (PS is a type of multiple criteria decision making (MCDM problem. In this study, we present a hybrid MCDM model combining the decision-making trial and evaluation laboratory (DEMATEL technique, analytic network process (ANP, and the VIKOR method to evaluate and improve Six Sigma projects for reducing performance gaps in each criterion and dimension. We consider the film printing industry of Taiwan as an empirical case. The results show that our study not only can use the best project selection, but can also be used to analyze the gaps between existing performance values and aspiration levels for improving the gaps in each dimension and criterion based on the influential network relation map.
Proposition of a multicriteria model to select logistics services providers
Directory of Open Access Journals (Sweden)
Miriam Catarina Soares Aharonovitz
2014-06-01
Full Text Available This study aims to propose a multicriteria model to select logistics service providers by the development of a decision tree. The methodology consists of a survey, which resulted in a sample of 181 responses. The sample was analyzed using statistic methods, descriptive statistics among them, multivariate analysis, variance analysis, and parametric tests to compare means. Based on these results, it was possible to obtain the decision tree and information to support the multicriteria analysis. The AHP (Analytic Hierarchy Process was applied to determine the data influence and thus ensure better consistency in the analysis. The decision tree categorizes the criteria according to the decision levels (strategic, tactical and operational. Furthermore, it allows to generically evaluate the importance of each criterion in the supplier selection process from the point of view of logistics services contractors.
A Reliability Based Model for Wind Turbine Selection
Directory of Open Access Journals (Sweden)
A.K. Rajeevan
2013-06-01
Full Text Available A wind turbine generator output at a specific site depends on many factors, particularly cut- in, rated and cut-out wind speed parameters. Hence power output varies from turbine to turbine. The objective of this paper is to develop a mathematical relationship between reliability and wind power generation. The analytical computation of monthly wind power is obtained from weibull statistical model using cubic mean cube root of wind speed. Reliability calculation is based on failure probability analysis. There are many different types of wind turbinescommercially available in the market. From reliability point of view, to get optimum reliability in power generation, it is desirable to select a wind turbine generator which is best suited for a site. The mathematical relationship developed in this paper can be used for site-matching turbine selection in reliability point of view.
Development of Solar Drying Model for Selected Cambodian Fish Species
Directory of Open Access Journals (Sweden)
Anna Hubackova
2014-01-01
Full Text Available A solar drying was investigated as one of perspective techniques for fish processing in Cambodia. The solar drying was compared to conventional drying in electric oven. Five typical Cambodian fish species were selected for this study. Mean solar drying temperature and drying air relative humidity were 55.6°C and 19.9%, respectively. The overall solar dryer efficiency was 12.37%, which is typical for natural convection solar dryers. An average evaporative capacity of solar dryer was 0.049 kg·h−1. Based on coefficient of determination (R2, chi-square (χ2 test, and root-mean-square error (RMSE, the most suitable models describing natural convection solar drying kinetics were Logarithmic model, Diffusion approximate model, and Two-term model for climbing perch and Nile tilapia, swamp eel and walking catfish and Channa fish, respectively. In case of electric oven drying, the Modified Page 1 model shows the best results for all investigated fish species except Channa fish where the two-term model is the best one. Sensory evaluation shows that most preferable fish is climbing perch, followed by Nile tilapia and walking catfish. This study brings new knowledge about drying kinetics of fresh water fish species in Cambodia and confirms the solar drying as acceptable technology for fish processing.
Fuzzy Goal Programming Approach in Selective Maintenance Reliability Model
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Neha Gupta
2013-12-01
Full Text Available 800x600 In the present paper, we have considered the allocation problem of repairable components for a parallel-series system as a multi-objective optimization problem and have discussed two different models. In first model the reliability of subsystems are considered as different objectives. In second model the cost and time spent on repairing the components are considered as two different objectives. These two models is formulated as multi-objective Nonlinear Programming Problem (MONLPP and a Fuzzy goal programming method is used to work out the compromise allocation in multi-objective selective maintenance reliability model in which we define the membership functions of each objective function and then transform membership functions into equivalent linear membership functions by first order Taylor series and finally by forming a fuzzy goal programming model obtain a desired compromise allocation of maintenance components. A numerical example is also worked out to illustrate the computational details of the method. Normal 0 false false false EN-US X-NONE X-NONE MicrosoftInternetExplorer4
Selection Strategies for Social Influence in the Threshold Model
Karampourniotis, Panagiotis; Szymanski, Boleslaw; Korniss, Gyorgy
The ubiquity of online social networks makes the study of social influence extremely significant for its applications to marketing, politics and security. Maximizing the spread of influence by strategically selecting nodes as initiators of a new opinion or trend is a challenging problem. We study the performance of various strategies for selection of large fractions of initiators on a classical social influence model, the Threshold model (TM). Under the TM, a node adopts a new opinion only when the fraction of its first neighbors possessing that opinion exceeds a pre-assigned threshold. The strategies we study are of two kinds: strategies based solely on the initial network structure (Degree-rank, Dominating Sets, PageRank etc.) and strategies that take into account the change of the states of the nodes during the evolution of the cascade, e.g. the greedy algorithm. We find that the performance of these strategies depends largely on both the network structure properties, e.g. the assortativity, and the distribution of the thresholds assigned to the nodes. We conclude that the optimal strategy needs to combine the network specifics and the model specific parameters to identify the most influential spreaders. Supported in part by ARL NS-CTA, ARO, and ONR.
Continuum model for chiral induced spin selectivity in helical molecules
Energy Technology Data Exchange (ETDEWEB)
Medina, Ernesto [Centro de Física, Instituto Venezolano de Investigaciones Científicas, 21827, Caracas 1020 A (Venezuela, Bolivarian Republic of); Groupe de Physique Statistique, Institut Jean Lamour, Université de Lorraine, 54506 Vandoeuvre-les-Nancy Cedex (France); Department of Chemistry and Biochemistry, Arizona State University, Tempe, Arizona 85287 (United States); González-Arraga, Luis A. [IMDEA Nanoscience, Cantoblanco, 28049 Madrid (Spain); Finkelstein-Shapiro, Daniel; Mujica, Vladimiro [Department of Chemistry and Biochemistry, Arizona State University, Tempe, Arizona 85287 (United States); Berche, Bertrand [Centro de Física, Instituto Venezolano de Investigaciones Científicas, 21827, Caracas 1020 A (Venezuela, Bolivarian Republic of); Groupe de Physique Statistique, Institut Jean Lamour, Université de Lorraine, 54506 Vandoeuvre-les-Nancy Cedex (France)
2015-05-21
A minimal model is exactly solved for electron spin transport on a helix. Electron transport is assumed to be supported by well oriented p{sub z} type orbitals on base molecules forming a staircase of definite chirality. In a tight binding interpretation, the spin-orbit coupling (SOC) opens up an effective π{sub z} − π{sub z} coupling via interbase p{sub x,y} − p{sub z} hopping, introducing spin coupled transport. The resulting continuum model spectrum shows two Kramers doublet transport channels with a gap proportional to the SOC. Each doubly degenerate channel satisfies time reversal symmetry; nevertheless, a bias chooses a transport direction and thus selects for spin orientation. The model predicts (i) which spin orientation is selected depending on chirality and bias, (ii) changes in spin preference as a function of input Fermi level and (iii) back-scattering suppression protected by the SO gap. We compute the spin current with a definite helicity and find it to be proportional to the torsion of the chiral structure and the non-adiabatic Aharonov-Anandan phase. To describe room temperature transport, we assume that the total transmission is the result of a product of coherent steps.
Selection of models to calculate the LLW source term
International Nuclear Information System (INIS)
Sullivan, T.M.
1991-10-01
Performance assessment of a LLW disposal facility begins with an estimation of the rate at which radionuclides migrate out of the facility (i.e., the source term). The focus of this work is to develop a methodology for calculating the source term. In general, the source term is influenced by the radionuclide inventory, the wasteforms and containers used to dispose of the inventory, and the physical processes that lead to release from the facility (fluid flow, container degradation, wasteform leaching, and radionuclide transport). In turn, many of these physical processes are influenced by the design of the disposal facility (e.g., infiltration of water). The complexity of the problem and the absence of appropriate data prevent development of an entirely mechanistic representation of radionuclide release from a disposal facility. Typically, a number of assumptions, based on knowledge of the disposal system, are used to simplify the problem. This document provides a brief overview of disposal practices and reviews existing source term models as background for selecting appropriate models for estimating the source term. The selection rationale and the mathematical details of the models are presented. Finally, guidance is presented for combining the inventory data with appropriate mechanisms describing release from the disposal facility. 44 refs., 6 figs., 1 tab
Variable Selection in Model-based Clustering: A General Variable Role Modeling
Maugis, Cathy; Celeux, Gilles; Martin-Magniette, Marie-Laure
2008-01-01
The currently available variable selection procedures in model-based clustering assume that the irrelevant clustering variables are all independent or are all linked with the relevant clustering variables. We propose a more versatile variable selection model which describes three possible roles for each variable: The relevant clustering variables, the irrelevant clustering variables dependent on a part of the relevant clustering variables and the irrelevant clustering variables totally indepe...
A Dual-Stage Two-Phase Model of Selective Attention
Hubner, Ronald; Steinhauser, Marco; Lehle, Carola
2010-01-01
The dual-stage two-phase (DSTP) model is introduced as a formal and general model of selective attention that includes both an early and a late stage of stimulus selection. Whereas at the early stage information is selected by perceptual filters whose selectivity is relatively limited, at the late stage stimuli are selected more efficiently on a…
Direction selectivity in a model of the starburst amacrine cell.
Tukker, John J; Taylor, W Rowland; Smith, Robert G
2004-01-01
The starburst amacrine cell (SBAC), found in all mammalian retinas, is thought to provide the directional inhibitory input recorded in On-Off direction-selective ganglion cells (DSGCs). While voltage recordings from the somas of SBACs have not shown robust direction selectivity (DS), the dendritic tips of these cells display direction-selective calcium signals, even when gamma-aminobutyric acid (GABAa,c) channels are blocked, implying that inhibition is not necessary to generate DS. This suggested that the distinctive morphology of the SBAC could generate a DS signal at the dendritic tips, where most of its synaptic output is located. To explore this possibility, we constructed a compartmental model incorporating realistic morphological structure, passive membrane properties, and excitatory inputs. We found robust DS at the dendritic tips but not at the soma. Two-spot apparent motion and annulus radial motion produced weak DS, but thin bars produced robust DS. For these stimuli, DS was caused by the interaction of a local synaptic input signal with a temporally delayed "global" signal, that is, an excitatory postsynaptic potential (EPSP) that spread from the activated inputs into the soma and throughout the dendritic tree. In the preferred direction the signals in the dendritic tips coincided, allowing summation, whereas in the null direction the local signal preceded the global signal, preventing summation. Sine-wave grating stimuli produced the greatest amount of DS, especially at high velocities and low spatial frequencies. The sine-wave DS responses could be accounted for by a simple mathematical model, which summed phase-shifted signals from soma and dendritic tip. By testing different artificial morphologies, we discovered DS was relatively independent of the morphological details, but depended on having a sufficient number of inputs at the distal tips and a limited electrotonic isolation. Adding voltage-gated calcium channels to the model showed that their
Parametric pattern selection in a reaction-diffusion model.
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Michael Stich
Full Text Available We compare spot patterns generated by Turing mechanisms with those generated by replication cascades, in a model one-dimensional reaction-diffusion system. We determine the stability region of spot solutions in parameter space as a function of a natural control parameter (feed-rate where degenerate patterns with different numbers of spots coexist for a fixed feed-rate. While it is possible to generate identical patterns via both mechanisms, we show that replication cascades lead to a wider choice of pattern profiles that can be selected through a tuning of the feed-rate, exploiting hysteresis and directionality effects of the different pattern pathways.
Estimation and variable selection for generalized additive partial linear models
Wang, Li
2011-08-01
We study generalized additive partial linear models, proposing the use of polynomial spline smoothing for estimation of nonparametric functions, and deriving quasi-likelihood based estimators for the linear parameters. We establish asymptotic normality for the estimators of the parametric components. The procedure avoids solving large systems of equations as in kernel-based procedures and thus results in gains in computational simplicity. We further develop a class of variable selection procedures for the linear parameters by employing a nonconcave penalized quasi-likelihood, which is shown to have an asymptotic oracle property. Monte Carlo simulations and an empirical example are presented for illustration. © Institute of Mathematical Statistics, 2011.
Modeling Knowledge Resource Selection in Expert Librarian Search
KAUFMAN, David R.; MEHRYAR, Maryam; CHASE, Herbert; HUNG, Peter; CHILOV, Marina; JOHNSON, Stephen B.; MENDONCA, Eneida
2011-01-01
Providing knowledge at the point of care offers the possibility for reducing error and improving patient outcomes. However, the vast majority of physician’s information needs are not met in a timely fashion. The research presented in this paper models an expert librarian’s search strategies as it pertains to the selection and use of various electronic information resources. The 10 searches conducted by the librarian to address physician’s information needs, varied in terms of complexity and question type. The librarian employed a total of 10 resources and used as many as 7 in a single search. The longer term objective is to model the sequential process in sufficient detail as to be able to contribute to the development of intelligent automated search agents. PMID:19380912
Two-Stage Hidden Markov Model in Gesture Recognition for Human Robot Interaction
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Nhan Nguyen-Duc-Thanh
2012-07-01
Full Text Available Hidden Markov Model (HMM is very rich in mathematical structure and hence can form the theoretical basis for use in a wide range of applications including gesture representation. Most research in this field, however, uses only HMM for recognizing simple gestures, while HMM can definitely be applied for whole gesture meaning recognition. This is very effectively applicable in Human-Robot Interaction (HRI. In this paper, we introduce an approach for HRI in which not only the human can naturally control the robot by hand gesture, but also the robot can recognize what kind of task it is executing. The main idea behind this method is the 2-stages Hidden Markov Model. The 1st HMM is to recognize the prime command-like gestures. Based on the sequence of prime gestures that are recognized from the 1st stage and which represent the whole action, the 2nd HMM plays a role in task recognition. Another contribution of this paper is that we use the output Mixed Gaussian distribution in HMM to improve the recognition rate. In the experiment, we also complete a comparison of the different number of hidden states and mixture components to obtain the optimal one, and compare to other methods to evaluate this performance.
Cliff-edge model of obstetric selection in humans.
Mitteroecker, Philipp; Huttegger, Simon M; Fischer, Barbara; Pavlicev, Mihaela
2016-12-20
The strikingly high incidence of obstructed labor due to the disproportion of fetal size and the mother's pelvic dimensions has puzzled evolutionary scientists for decades. Here we propose that these high rates are a direct consequence of the distinct characteristics of human obstetric selection. Neonatal size relative to the birth-relevant maternal dimensions is highly variable and positively associated with reproductive success until it reaches a critical value, beyond which natural delivery becomes impossible. As a consequence, the symmetric phenotype distribution cannot match the highly asymmetric, cliff-edged fitness distribution well: The optimal phenotype distribution that maximizes population mean fitness entails a fraction of individuals falling beyond the "fitness edge" (i.e., those with fetopelvic disproportion). Using a simple mathematical model, we show that weak directional selection for a large neonate, a narrow pelvic canal, or both is sufficient to account for the considerable incidence of fetopelvic disproportion. Based on this model, we predict that the regular use of Caesarean sections throughout the last decades has led to an evolutionary increase of fetopelvic disproportion rates by 10 to 20%.
Developing a conceptual model for selecting and evaluating online markets
Directory of Open Access Journals (Sweden)
Sadegh Feizollahi
2013-04-01
Full Text Available There are many evidences, which emphasis on the benefits of using new technologies of information and communication in international business and many believe that E-Commerce can help satisfy customer explicit and implicit requirements. Internet shopping is a concept developed after the introduction of electronic commerce. Information technology (IT and its applications, specifically in the realm of the internet and e-mail promoted the development of e-commerce in terms of advertising, motivating and information. However, with the development of new technologies, credit and financial exchange on the internet websites were constructed so to facilitate e-commerce. The proposed study sends a total of 200 questionnaires to the target group (teachers - students - professionals - managers of commercial web sites and it manages to collect 130 questionnaires for final evaluation. Cronbach's alpha test is used for measuring reliability and to evaluate the validity of measurement instruments (questionnaires, and to assure construct validity, confirmatory factor analysis is employed. In addition, in order to analyze the research questions based on the path analysis method and to determine markets selection models, a regular technique is implemented. In the present study, after examining different aspects of e-commerce, we provide a conceptual model for selecting and evaluating online marketing in Iran. These findings provide a consistent, targeted and holistic framework for the development of the Internet market in the country.
Ensemble Prediction Model with Expert Selection for Electricity Price Forecasting
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Bijay Neupane
2017-01-01
Full Text Available Forecasting of electricity prices is important in deregulated electricity markets for all of the stakeholders: energy wholesalers, traders, retailers and consumers. Electricity price forecasting is an inherently difficult problem due to its special characteristic of dynamicity and non-stationarity. In this paper, we present a robust price forecasting mechanism that shows resilience towards the aggregate demand response effect and provides highly accurate forecasted electricity prices to the stakeholders in a dynamic environment. We employ an ensemble prediction model in which a group of different algorithms participates in forecasting 1-h ahead the price for each hour of a day. We propose two different strategies, namely, the Fixed Weight Method (FWM and the Varying Weight Method (VWM, for selecting each hour’s expert algorithm from the set of participating algorithms. In addition, we utilize a carefully engineered set of features selected from a pool of features extracted from the past electricity price data, weather data and calendar data. The proposed ensemble model offers better results than the Autoregressive Integrated Moving Average (ARIMA method, the Pattern Sequence-based Forecasting (PSF method and our previous work using Artificial Neural Networks (ANN alone on the datasets for New York, Australian and Spanish electricity markets.
A Network Analysis Model for Selecting Sustainable Technology
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Sangsung Park
2015-09-01
Full Text Available Most companies develop technologies to improve their competitiveness in the marketplace. Typically, they then patent these technologies around the world in order to protect their intellectual property. Other companies may use patented technologies to develop new products, but must pay royalties to the patent holders or owners. Should they fail to do so, this can result in legal disputes in the form of patent infringement actions between companies. To avoid such situations, companies attempt to research and develop necessary technologies before their competitors do so. An important part of this process is analyzing existing patent documents in order to identify emerging technologies. In such analyses, extracting sustainable technology from patent data is important, because sustainable technology drives technological competition among companies and, thus, the development of new technologies. In addition, selecting sustainable technologies makes it possible to plan their R&D (research and development efficiently. In this study, we propose a network model that can be used to select the sustainable technology from patent documents, based on the centrality and degree of a social network analysis. To verify the performance of the proposed model, we carry out a case study using actual patent data from patent databases.
DEFF Research Database (Denmark)
Yang, Ziheng; Nielsen, Rasmus
2008-01-01
to examine the null hypothesis that codon usage is due to mutation bias alone, not influenced by natural selection. Application of the test to the mammalian data led to rejection of the null hypothesis in most genes, suggesting that natural selection may be a driving force in the evolution of synonymous......Current models of codon substitution are formulated at the levels of nucleotide substitution and do not explicitly consider the separate effects of mutation and selection. They are thus incapable of inferring whether mutation or selection is responsible for evolution at silent sites. Here we...... implement a few population genetics models of codon substitution that explicitly consider mutation bias and natural selection at the DNA level. Selection on codon usage is modeled by introducing codon-fitness parameters, which together with mutation-bias parameters, predict optimal codon frequencies...
Austin, Peter C
2008-10-01
Researchers have proposed using bootstrap resampling in conjunction with automated variable selection methods to identify predictors of an outcome and to develop parsimonious regression models. Using this method, multiple bootstrap samples are drawn from the original data set. Traditional backward variable elimination is used in each bootstrap sample, and the proportion of bootstrap samples in which each candidate variable is identified as an independent predictor of the outcome is determined. The performance of this method for identifying predictor variables has not been examined. Monte Carlo simulation methods were used to determine the ability of bootstrap model selection methods to correctly identify predictors of an outcome when those variables that are selected for inclusion in at least 50% of the bootstrap samples are included in the final regression model. We compared the performance of the bootstrap model selection method to that of conventional backward variable elimination. Bootstrap model selection tended to result in an approximately equal proportion of selected models being equal to the true regression model compared with the use of conventional backward variable elimination. Bootstrap model selection performed comparatively to backward variable elimination for identifying the true predictors of a binary outcome.
Incorporating Pass-Phrase Dependent Background Models for Text-Dependent Speaker verification
DEFF Research Database (Denmark)
Sarkar, Achintya Kumar; Tan, Zheng-Hua
2018-01-01
is compared to conventional text-independent background model based TD-SV systems using either Gaussian mixture model (GMM)-universal background model (UBM) or Hidden Markov model (HMM)-UBM or i-vector paradigms. In addition, we consider two approaches to build PBMs: speaker-independent and speaker......In this paper, we propose pass-phrase dependent background models (PBMs) for text-dependent (TD) speaker verification (SV) to integrate the pass-phrase identification process into the conventional TD-SV system, where a PBM is derived from a text-independent background model through adaptation using...... and the selected PBM is then used for the log likelihood ratio (LLR) calculation with respect to the claimant model. The proposed method incorporates the pass-phrase identification step in the LLR calculation, which is not considered in conventional standalone TD-SV systems. The performance of the proposed method...
A CONCEPTUAL MODEL FOR IMPROVED PROJECT SELECTION AND PRIORITISATION
Directory of Open Access Journals (Sweden)
P. J. Viljoen
2012-01-01
Full Text Available
ENGLISH ABSTRACT: Project portfolio management processes are often designed and operated as a series of stages (or project phases and gates. However, the flow of such a process is often slow, characterised by queues waiting for a gate decision and by repeated work from previous stages waiting for additional information or for re-processing. In this paper the authors propose a conceptual model that applies supply chain and constraint management principles to the project portfolio management process. An advantage of the proposed model is that it provides the ability to select and prioritise projects without undue changes to project schedules. This should result in faster flow through the system.
AFRIKAANSE OPSOMMING: Prosesse om portefeuljes van projekte te bestuur word normaalweg ontwerp en bedryf as ’n reeks fases en hekke. Die vloei deur so ’n proses is dikwels stadig en word gekenmerk deur toue wat wag vir besluite by die hekke en ook deur herwerk van vorige fases wat wag vir verdere inligting of vir herprosessering. In hierdie artikel word ‘n konseptuele model voorgestel. Die model berus op die beginsels van voorsieningskettings sowel as van beperkingsbestuur, en bied die voordeel dat projekte geselekteer en geprioritiseer kan word sonder onnodige veranderinge aan projekskedules. Dit behoort te lei tot versnelde vloei deur die stelsel.
On model selections for repeated measurement data in clinical studies.
Zou, Baiming; Jin, Bo; Koch, Gary G; Zhou, Haibo; Borst, Stephen E; Menon, Sandeep; Shuster, Jonathan J
2015-05-10
Repeated measurement designs have been widely used in various randomized controlled trials for evaluating long-term intervention efficacies. For some clinical trials, the primary research question is how to compare two treatments at a fixed time, using a t-test. Although simple, robust, and convenient, this type of analysis fails to utilize a large amount of collected information. Alternatively, the mixed-effects model is commonly used for repeated measurement data. It models all available data jointly and allows explicit assessment of the overall treatment effects across the entire time spectrum. In this paper, we propose an analytic strategy for longitudinal clinical trial data where the mixed-effects model is coupled with a model selection scheme. The proposed test statistics not only make full use of all available data but also utilize the information from the optimal model deemed for the data. The performance of the proposed method under various setups, including different data missing mechanisms, is evaluated via extensive Monte Carlo simulations. Our numerical results demonstrate that the proposed analytic procedure is more powerful than the t-test when the primary interest is to test for the treatment effect at the last time point. Simulations also reveal that the proposed method outperforms the usual mixed-effects model for testing the overall treatment effects across time. In addition, the proposed framework is more robust and flexible in dealing with missing data compared with several competing methods. The utility of the proposed method is demonstrated by analyzing a clinical trial on the cognitive effect of testosterone in geriatric men with low baseline testosterone levels. Copyright © 2015 John Wiley & Sons, Ltd.
Computationally efficient thermal-mechanical modelling of selective laser melting
Yang, Yabin; Ayas, Can
2017-10-01
The Selective laser melting (SLM) is a powder based additive manufacturing (AM) method to produce high density metal parts with complex topology. However, part distortions and accompanying residual stresses deteriorates the mechanical reliability of SLM products. Modelling of the SLM process is anticipated to be instrumental for understanding and predicting the development of residual stress field during the build process. However, SLM process modelling requires determination of the heat transients within the part being built which is coupled to a mechanical boundary value problem to calculate displacement and residual stress fields. Thermal models associated with SLM are typically complex and computationally demanding. In this paper, we present a simple semi-analytical thermal-mechanical model, developed for SLM that represents the effect of laser scanning vectors with line heat sources. The temperature field within the part being build is attained by superposition of temperature field associated with line heat sources in a semi-infinite medium and a complimentary temperature field which accounts for the actual boundary conditions. An analytical solution of a line heat source in a semi-infinite medium is first described followed by the numerical procedure used for finding the complimentary temperature field. This analytical description of the line heat sources is able to capture the steep temperature gradients in the vicinity of the laser spot which is typically tens of micrometers. In turn, semi-analytical thermal model allows for having a relatively coarse discretisation of the complimentary temperature field. The temperature history determined is used to calculate the thermal strain induced on the SLM part. Finally, a mechanical model governed by elastic-plastic constitutive rule having isotropic hardening is used to predict the residual stresses.
Partially Hidden Markov Models
DEFF Research Database (Denmark)
Forchhammer, Søren Otto; Rissanen, Jorma
1996-01-01
Partially Hidden Markov Models (PHMM) are introduced. They differ from the ordinary HMM's in that both the transition probabilities of the hidden states and the output probabilities are conditioned on past observations. As an illustration they are applied to black and white image compression where...
Patch-based generative shape model and MDL model selection for statistical analysis of archipelagos
DEFF Research Database (Denmark)
Ganz, Melanie; Nielsen, Mads; Brandt, Sami
2010-01-01
as a probability distribution of a binary image where the model is intended to facilitate sequential simulation. Our results show that a relatively simple model is able to generate structures visually similar to calcifications. Furthermore, we used the shape model as a shape prior in the statistical segmentation......We propose a statistical generative shape model for archipelago-like structures. These kind of structures occur, for instance, in medical images, where our intention is to model the appearance and shapes of calcifications in x-ray radio graphs. The generative model is constructed by (1) learning...... a patch-based dictionary for possible shapes, (2) building up a time-homogeneous Markov model to model the neighbourhood correlations between the patches, and (3) automatic selection of the model complexity by the minimum description length principle. The generative shape model is proposed...
Consistency in Estimation and Model Selection of Dynamic Panel Data Models with Fixed Effects
Directory of Open Access Journals (Sweden)
Guangjie Li
2015-07-01
Full Text Available We examine the relationship between consistent parameter estimation and model selection for autoregressive panel data models with fixed effects. We find that the transformation of fixed effects proposed by Lancaster (2002 does not necessarily lead to consistent estimation of common parameters when some true exogenous regressors are excluded. We propose a data dependent way to specify the prior of the autoregressive coefficient and argue for comparing different model specifications before parameter estimation. Model selection properties of Bayes factors and Bayesian information criterion (BIC are investigated. When model uncertainty is substantial, we recommend the use of Bayesian Model Averaging to obtain point estimators with lower root mean squared errors (RMSE. We also study the implications of different levels of inclusion probabilities by simulations.
Multicriteria decision group model for the selection of suppliers
Directory of Open Access Journals (Sweden)
Luciana Hazin Alencar
2008-08-01
Full Text Available Several authors have been studying group decision making over the years, which indicates how relevant it is. This paper presents a multicriteria group decision model based on ELECTRE IV and VIP Analysis methods, to those cases where there is great divergence among the decision makers. This model includes two stages. In the first, the ELECTRE IV method is applied and a collective criteria ranking is obtained. In the second, using criteria ranking, VIP Analysis is applied and the alternatives are selected. To illustrate the model, a numerical application in the context of the selection of suppliers in project management is used. The suppliers that form part of the project team have a crucial role in project management. They are involved in a network of connected activities that can jeopardize the success of the project, if they are not undertaken in an appropriate way. The question tackled is how to select service suppliers for a project on behalf of an enterprise that assists the multiple objectives of the decision-makers.Vários autores têm estudado decisão em grupo nos últimos anos, o que indica a relevância do assunto. Esse artigo apresenta um modelo multicritério de decisão em grupo baseado nos métodos ELECTRE IV e VIP Analysis, adequado aos casos em que se tem uma grande divergência entre os decisores. Esse modelo é composto por dois estágios. No primeiro, o método ELECTRE IV é aplicado e uma ordenação dos critérios é obtida. No próximo estágio, com a ordenação dos critérios, o método VIP Analysis é aplicado e as alternativas são selecionadas. Para ilustrar o modelo, uma aplicação numérica no contexto da seleção de fornecedores em projetos é realizada. Os fornecedores que fazem parte da equipe do projeto têm um papel fundamental no gerenciamento de projetos. Eles estão envolvidos em uma rede de atividades conectadas que, caso não sejam executadas de forma apropriada, podem colocar em risco o sucesso do
Improving permafrost distribution modelling using feature selection algorithms
Deluigi, Nicola; Lambiel, Christophe; Kanevski, Mikhail
2016-04-01
The availability of an increasing number of spatial data on the occurrence of mountain permafrost allows the employment of machine learning (ML) classification algorithms for modelling the distribution of the phenomenon. One of the major problems when dealing with high-dimensional dataset is the number of input features (variables) involved. Application of ML classification algorithms to this large number of variables leads to the risk of overfitting, with the consequence of a poor generalization/prediction. For this reason, applying feature selection (FS) techniques helps simplifying the amount of factors required and improves the knowledge on adopted features and their relation with the studied phenomenon. Moreover, taking away irrelevant or redundant variables from the dataset effectively improves the quality of the ML prediction. This research deals with a comparative analysis of permafrost distribution models supported by FS variable importance assessment. The input dataset (dimension = 20-25, 10 m spatial resolution) was constructed using landcover maps, climate data and DEM derived variables (altitude, aspect, slope, terrain curvature, solar radiation, etc.). It was completed with permafrost evidences (geophysical and thermal data and rock glacier inventories) that serve as training permafrost data. Used FS algorithms informed about variables that appeared less statistically important for permafrost presence/absence. Three different algorithms were compared: Information Gain (IG), Correlation-based Feature Selection (CFS) and Random Forest (RF). IG is a filter technique that evaluates the worth of a predictor by measuring the information gain with respect to the permafrost presence/absence. Conversely, CFS is a wrapper technique that evaluates the worth of a subset of predictors by considering the individual predictive ability of each variable along with the degree of redundancy between them. Finally, RF is a ML algorithm that performs FS as part of its
Multiphysics modeling of selective laser sintering/melting
Ganeriwala, Rishi Kumar
A significant percentage of total global employment is due to the manufacturing industry. However, manufacturing also accounts for nearly 20% of total energy usage in the United States according to the EIA. In fact, manufacturing accounted for 90% of industrial energy consumption and 84% of industry carbon dioxide emissions in 2002. Clearly, advances in manufacturing technology and efficiency are necessary to curb emissions and help society as a whole. Additive manufacturing (AM) refers to a relatively recent group of manufacturing technologies whereby one can 3D print parts, which has the potential to significantly reduce waste, reconfigure the supply chain, and generally disrupt the whole manufacturing industry. Selective laser sintering/melting (SLS/SLM) is one type of AM technology with the distinct advantage of being able to 3D print metals and rapidly produce net shape parts with complicated geometries. In SLS/SLM parts are built up layer-by-layer out of powder particles, which are selectively sintered/melted via a laser. However, in order to produce defect-free parts of sufficient strength, the process parameters (laser power, scan speed, layer thickness, powder size, etc.) must be carefully optimized. Obviously, these process parameters will vary depending on material, part geometry, and desired final part characteristics. Running experiments to optimize these parameters is costly, energy intensive, and extremely material specific. Thus a computational model of this process would be highly valuable. In this work a three dimensional, reduced order, coupled discrete element - finite difference model is presented for simulating the deposition and subsequent laser heating of a layer of powder particles sitting on top of a substrate. Validation is provided and parameter studies are conducted showing the ability of this model to help determine appropriate process parameters and an optimal powder size distribution for a given material. Next, thermal stresses upon
Hyperopt: a Python library for model selection and hyperparameter optimization
Bergstra, James; Komer, Brent; Eliasmith, Chris; Yamins, Dan; Cox, David D.
2015-01-01
Sequential model-based optimization (also known as Bayesian optimization) is one of the most efficient methods (per function evaluation) of function minimization. This efficiency makes it appropriate for optimizing the hyperparameters of machine learning algorithms that are slow to train. The Hyperopt library provides algorithms and parallelization infrastructure for performing hyperparameter optimization (model selection) in Python. This paper presents an introductory tutorial on the usage of the Hyperopt library, including the description of search spaces, minimization (in serial and parallel), and the analysis of the results collected in the course of minimization. This paper also gives an overview of Hyperopt-Sklearn, a software project that provides automatic algorithm configuration of the Scikit-learn machine learning library. Following Auto-Weka, we take the view that the choice of classifier and even the choice of preprocessing module can be taken together to represent a single large hyperparameter optimization problem. We use Hyperopt to define a search space that encompasses many standard components (e.g. SVM, RF, KNN, PCA, TFIDF) and common patterns of composing them together. We demonstrate, using search algorithms in Hyperopt and standard benchmarking data sets (MNIST, 20-newsgroups, convex shapes), that searching this space is practical and effective. In particular, we improve on best-known scores for the model space for both MNIST and convex shapes. The paper closes with some discussion of ongoing and future work.
Estimating a dynamic model of sex selection in China.
Ebenstein, Avraham
2011-05-01
High ratios of males to females in China, which have historically concerned researchers (Sen 1990), have increased in the wake of China's one-child policy, which began in 1979. Chinese policymakers are currently attempting to correct the imbalance in the sex ratio through initiatives that provide financial compensation to parents with daughters. Other scholars have advocated a relaxation of the one-child policy to allow more parents to have a son without engaging in sex selection. In this article, I present a model of fertility choice when parents have access to a sex-selection technology and face a mandated fertility limit. By exploiting variation in fines levied in China for unsanctioned births, I estimate the relative price of a son and daughter for mothers observed in China's census data (1982-2000). I find that a couple's first son is worth 1.42 years of income more than a first daughter, and the premium is highest among less-educated mothers and families engaged in agriculture. Simulations indicate that a subsidy of 1 year of income to families without a son would reduce the number of "missing girls" by 67% but impose an annual cost of 1.8% of Chinese gross domestic product (GDP). Alternatively, a three-child policy would reduce the number of "missing girls" by 56% but increase the fertility rate by 35%.
Model catalysis by size-selected cluster deposition
Energy Technology Data Exchange (ETDEWEB)
Anderson, Scott [Univ. of Utah, Salt Lake City, UT (United States)
2015-11-20
This report summarizes the accomplishments during the last four years of the subject grant. Results are presented for experiments in which size-selected model catalysts were studied under surface science and aqueous electrochemical conditions. Strong effects of cluster size were found, and by correlating the size effects with size-dependent physical properties of the samples measured by surface science methods, it was possible to deduce mechanistic insights, such as the factors that control the rate-limiting step in the reactions. Results are presented for CO oxidation, CO binding energetics and geometries, and electronic effects under surface science conditions, and for the electrochemical oxygen reduction reaction, ethanol oxidation reaction, and for oxidation of carbon by water.
Analytical Modelling Of Milling For Tool Design And Selection
Fontaine, M.; Devillez, A.; Dudzinski, D.
2007-05-01
This paper presents an efficient analytical model which allows to simulate a large panel of milling operations. A geometrical description of common end mills and of their engagement in the workpiece material is proposed. The internal radius of the rounded part of the tool envelope is used to define the considered type of mill. The cutting edge position is described for a constant lead helix and for a constant local helix angle. A thermomechanical approach of oblique cutting is applied to predict forces acting on the tool and these results are compared with experimental data obtained from milling tests on a 42CrMo4 steel for three classical types of mills. The influence of some tool's geometrical parameters on predicted cutting forces is presented in order to propose optimisation criteria for design and selection of cutting tools.
Analytical Modelling Of Milling For Tool Design And Selection
International Nuclear Information System (INIS)
Fontaine, M.; Devillez, A.; Dudzinski, D.
2007-01-01
This paper presents an efficient analytical model which allows to simulate a large panel of milling operations. A geometrical description of common end mills and of their engagement in the workpiece material is proposed. The internal radius of the rounded part of the tool envelope is used to define the considered type of mill. The cutting edge position is described for a constant lead helix and for a constant local helix angle. A thermomechanical approach of oblique cutting is applied to predict forces acting on the tool and these results are compared with experimental data obtained from milling tests on a 42CrMo4 steel for three classical types of mills. The influence of some tool's geometrical parameters on predicted cutting forces is presented in order to propose optimisation criteria for design and selection of cutting tools
Word and Subword Modelling in a Segment-Based HMM Word Spotter Using a Data Analytic Approach
1992-09-01
Appendix C 15 ous speaker-adaptation schemes (Stern 87, Rigoll 89, Rtischev 89, Kubala 90, Stern 91, Bellegarda 92] and the introduction of...March, 1992. ( Rigoll 891 Rigoll , G., "Speaker Adaptation for Large Vocabulary Speech Recognition System using ’Speaker Markov Mod- els’," Proc. ICASSP 89
Surfleet, Christopher G.; Tullos, Desirèe; Chang, Heejun; Jung, Il-Won
2012-09-01
SummaryA wide variety of approaches to hydrologic (rainfall-runoff) modeling of river basins confounds our ability to select, develop, and interpret models, particularly in the evaluation of prediction uncertainty associated with climate change assessment. To inform the model selection process, we characterized and compared three structurally-distinct approaches and spatial scales of parameterization to modeling catchment hydrology: a large-scale approach (using the VIC model; 671,000 km2 area), a basin-scale approach (using the PRMS model; 29,700 km2 area), and a site-specific approach (the GSFLOW model; 4700 km2 area) forced by the same future climate estimates. For each approach, we present measures of fit to historic observations and predictions of future response, as well as estimates of model parameter uncertainty, when available. While the site-specific approach generally had the best fit to historic measurements, the performance of the model approaches varied. The site-specific approach generated the best fit at unregulated sites, the large scale approach performed best just downstream of flood control projects, and model performance varied at the farthest downstream sites where streamflow regulation is mitigated to some extent by unregulated tributaries and water diversions. These results illustrate how selection of a modeling approach and interpretation of climate change projections require (a) appropriate parameterization of the models for climate and hydrologic processes governing runoff generation in the area under study, (b) understanding and justifying the assumptions and limitations of the model, and (c) estimates of uncertainty associated with the modeling approach.
Evaluating experimental design for soil-plant model selection with Bayesian model averaging
Wöhling, Thomas; Geiges, Andreas; Nowak, Wolfgang; Gayler, Sebastian
2013-04-01
The objective selection of appropriate models for realistic simulations of coupled soil-plant processes is a challenging task since the processes are complex, not fully understood at larger scales, and highly non-linear. Also, comprehensive data sets are scarce, and measurements are uncertain. In the past decades, a variety of different models have been developed that exhibit a wide range of complexity regarding their approximation of processes in the coupled model compartments. We present a method for evaluating experimental design for maximum confidence in the model selection task. The method considers uncertainty in parameters, measurements and model structures. Advancing the ideas behind Bayesian Model Averaging (BMA), the model weights in BMA are perceived as uncertain quantities with assigned probability distributions that narrow down as more data are made available. This allows assessing the power of different data types, data densities and data locations in identifying the best model structure from among a suite of plausible models. The models considered in this study are the crop models CERES, SUCROS, GECROS and SPASS, which are coupled to identical routines for simulating soil processes within the modelling framework Expert-N. The four models considerably differ in the degree of detail at which crop growth and root water uptake are represented. Monte-Carlo simulations were conducted for each of these models considering their uncertainty in soil hydraulic properties and selected crop model parameters. The models were then conditioned on field measurements of soil moisture, leaf-area index (LAI), and evapotranspiration rates (from eddy-covariance measurements) during a vegetation period of winter wheat at the Nellingen site in Southwestern Germany. Following our new method, we derived the BMA model weights (and their distributions) when using all data or different subsets thereof. We discuss to which degree the posterior BMA mean outperformed the prior BMA
Ponciano, José Miguel; Taper, Mark L; Dennis, Brian; Lele, Subhash R
2009-02-01
Hierarchical statistical models are increasingly being used to describe complex ecological processes. The data cloning (DC) method is a new general technique that uses Markov chain Monte Carlo (MCMC) algorithms to compute maximum likelihood (ML) estimates along with their asymptotic variance estimates for hierarchical models. Despite its generality, the method has two inferential limitations. First, it only provides Wald-type confidence intervals, known to be inaccurate in small samples. Second, it only yields ML parameter estimates, but not the maximized likelihood values used for profile likelihood intervals, likelihood ratio hypothesis tests, and information-theoretic model selection. Here we describe how to overcome these inferential limitations with a computationally efficient method for calculating likelihood ratios via data cloning. The ability to calculate likelihood ratios allows one to do hypothesis tests, construct accurate confidence intervals and undertake information-based model selection with hierarchical models in a frequentist context. To demonstrate the use of these tools with complex ecological models, we reanalyze part of Gause's classic Paramecium data with state-space population models containing both environmental noise and sampling error. The analysis results include improved confidence intervals for parameters, a hypothesis test of laboratory replication, and a comparison of the Beverton-Holt and the Ricker growth forms based on a model selection index.
Model Selection in the Analysis of Photoproduction Data
Landay, Justin
2017-01-01
Scattering experiments provide one of the most powerful and useful tools for probing matter to better understand its fundamental properties governed by the strong interaction. As the spectroscopy of the excited states of nucleons enters a new era of precision ushered in by improved experiments at Jefferson Lab and other facilities around the world, traditional partial-wave analysis methods must be adjusted accordingly. In this poster, we present a rigorous set of statistical tools and techniques that we implemented; most notably, the LASSO method, which serves for the selection of the simplest model, allowing us to avoid over fitting. In the case of establishing the spectrum of exited baryons, it avoids overpopulation of the spectrum and thus the occurrence of false-positives. This is a prerequisite to reliably compare theories like lattice QCD or quark models to experiments. Here, we demonstrate the principle by simultaneously fitting three observables in neutral pion photo-production, such as the differential cross section, beam asymmetry and target polarization across thousands of data points. Other authors include Michael Doring, Bin Hu, and Raquel Molina.
Wentworth, Mami Tonoe
Uncertainty quantification plays an important role when making predictive estimates of model responses. In this context, uncertainty quantification is defined as quantifying and reducing uncertainties, and the objective is to quantify uncertainties in parameter, model and measurements, and propagate the uncertainties through the model, so that one can make a predictive estimate with quantified uncertainties. Two of the aspects of uncertainty quantification that must be performed prior to propagating uncertainties are model calibration and parameter selection. There are several efficient techniques for these processes; however, the accuracy of these methods are often not verified. This is the motivation for our work, and in this dissertation, we present and illustrate verification frameworks for model calibration and parameter selection in the context of biological and physical models. First, HIV models, developed and improved by [2, 3, 8], describe the viral infection dynamics of an HIV disease. These are also used to make predictive estimates of viral loads and T-cell counts and to construct an optimal control for drug therapy. Estimating input parameters is an essential step prior to uncertainty quantification. However, not all the parameters are identifiable, implying that they cannot be uniquely determined by the observations. These unidentifiable parameters can be partially removed by performing parameter selection, a process in which parameters that have minimal impacts on the model response are determined. We provide verification techniques for Bayesian model calibration and parameter selection for an HIV model. As an example of a physical model, we employ a heat model with experimental measurements presented in [10]. A steady-state heat model represents a prototypical behavior for heat conduction and diffusion process involved in a thermal-hydraulic model, which is a part of nuclear reactor models. We employ this simple heat model to illustrate verification
A simple model of group selection that cannot be analyzed with inclusive fitness
van Veelen, M.; Luo, S.; Simon, B.
2014-01-01
A widespread claim in evolutionary theory is that every group selection model can be recast in terms of inclusive fitness. Although there are interesting classes of group selection models for which this is possible, we show that it is not true in general. With a simple set of group selection models,
Schöniger, Anneli; Wöhling, Thomas; Samaniego, Luis; Nowak, Wolfgang
2014-12-01
Bayesian model selection or averaging objectively ranks a number of plausible, competing conceptual models based on Bayes' theorem. It implicitly performs an optimal trade-off between performance in fitting available data and minimum model complexity. The procedure requires determining Bayesian model evidence (BME), which is the likelihood of the observed data integrated over each model's parameter space. The computation of this integral is highly challenging because it is as high-dimensional as the number of model parameters. Three classes of techniques to compute BME are available, each with its own challenges and limitations: (1) Exact and fast analytical solutions are limited by strong assumptions. (2) Numerical evaluation quickly becomes unfeasible for expensive models. (3) Approximations known as information criteria (ICs) such as the AIC, BIC, or KIC (Akaike, Bayesian, or Kashyap information criterion, respectively) yield contradicting results with regard to model ranking. Our study features a theory-based intercomparison of these techniques. We further assess their accuracy in a simplistic synthetic example where for some scenarios an exact analytical solution exists. In more challenging scenarios, we use a brute-force Monte Carlo integration method as reference. We continue this analysis with a real-world application of hydrological model selection. This is a first-time benchmarking of the various methods for BME evaluation against true solutions. Results show that BME values from ICs are often heavily biased and that the choice of approximation method substantially influences the accuracy of model ranking. For reliable model selection, bias-free numerical methods should be preferred over ICs whenever computationally feasible.
Detecting Structural Breaks using Hidden Markov Models
DEFF Research Database (Denmark)
Ntantamis, Christos
Testing for structural breaks and identifying their location is essential for econometric modeling. In this paper, a Hidden Markov Model (HMM) approach is used in order to perform these tasks. Breaks are defined as the data points where the underlying Markov Chain switches from one state to another....... The estimation of the HMM is conducted using a variant of the Iterative Conditional Expectation-Generalized Mixture (ICE-GEMI) algorithm proposed by Delignon et al. (1997), that permits analysis of the conditional distributions of economic data and allows for different functional forms across regimes...
Hidden Markov models for labeled sequences
DEFF Research Database (Denmark)
Krogh, Anders Stærmose
1994-01-01
A hidden Markov model for labeled observations, called a class HMM, is introduced and a maximum likelihood method is developed for estimating the parameters of the model. Instead of training it to model the statistics of the training sequences it is trained to optimize recognition. It resembles MMI...
Heat transfer modelling and stability analysis of selective laser melting
International Nuclear Information System (INIS)
Gusarov, A.V.; Yadroitsev, I.; Bertrand, Ph.; Smurov, I.
2007-01-01
The process of direct manufacturing by selective laser melting basically consists of laser beam scanning over a thin powder layer deposited on a dense substrate. Complete remelting of the powder in the scanned zone and its good adhesion to the substrate ensure obtaining functional parts with improved mechanical properties. Experiments with single-line scanning indicate, that an interval of scanning velocities exists where the remelted tracks are uniform. The tracks become broken if the scanning velocity is outside this interval. This is extremely undesirable and referred to as the 'balling' effect. A numerical model of coupled radiation and heat transfer is proposed to analyse the observed instability. The 'balling' effect at high scanning velocities (above ∼20 cm/s for the present conditions) can be explained by the Plateau-Rayleigh capillary instability of the melt pool. Two factors stabilize the process with decreasing the scanning velocity: reducing the length-to-width ratio of the melt pool and increasing the width of its contact with the substrate
Model Selection and Hypothesis Testing for Large-Scale Network Models with Overlapping Groups
Directory of Open Access Journals (Sweden)
Tiago P. Peixoto
2015-03-01
Full Text Available The effort to understand network systems in increasing detail has resulted in a diversity of methods designed to extract their large-scale structure from data. Unfortunately, many of these methods yield diverging descriptions of the same network, making both the comparison and understanding of their results a difficult challenge. A possible solution to this outstanding issue is to shift the focus away from ad hoc methods and move towards more principled approaches based on statistical inference of generative models. As a result, we face instead the more well-defined task of selecting between competing generative processes, which can be done under a unified probabilistic framework. Here, we consider the comparison between a variety of generative models including features such as degree correction, where nodes with arbitrary degrees can belong to the same group, and community overlap, where nodes are allowed to belong to more than one group. Because such model variants possess an increasing number of parameters, they become prone to overfitting. In this work, we present a method of model selection based on the minimum description length criterion and posterior odds ratios that is capable of fully accounting for the increased degrees of freedom of the larger models and selects the best one according to the statistical evidence available in the data. In applying this method to many empirical unweighted networks from different fields, we observe that community overlap is very often not supported by statistical evidence and is selected as a better model only for a minority of them. On the other hand, we find that degree correction tends to be almost universally favored by the available data, implying that intrinsic node proprieties (as opposed to group properties are often an essential ingredient of network formation.
Määttä, Anu; Laine, Marko; Tamminen, Johanna
2015-04-01
This study aims to characterize the uncertainty related to the aerosol microphysical model selection and the modelling error due to approximations in the forward modelling. Many satellite aerosol retrieval algorithms rely on pre-calculated look-up tables of model parameters representing various atmospheric conditions. In the retrieval we need to choose the most appropriate aerosol microphysical models from the pre-defined set of models by fitting them to the observations. The aerosol properties, e.g. AOD, are then determined from the best models. This choice of an appropriate aerosol model composes a notable part in the AOD retrieval uncertainty. The motivation in our study was to account these two sources in the total uncertainty budget: uncertainty in selecting the most appropriate model, and uncertainty resulting from the approximations in the pre-calculated aerosol microphysical model. The systematic model error was analysed by studying the behaviour of the model residuals, i.e. the differences between modelled and observed reflectances, by statistical methods. We utilised Gaussian processes to characterize the uncertainty related to approximations in aerosol microphysics modelling due to use of look-up tables and other non-modelled systematic features in the Level 1 data. The modelling error is described by a non-diagonal covariance matrix parameterised by correlation length, which is estimated from the residuals using computational tools from spatial statistics. In addition, we utilised Bayesian model selection and model averaging methods to account the uncertainty due to aerosol model selection. By acknowledging the modelling error as a source of uncertainty in the retrieval of AOD from observed spectral reflectance, we allow the observed values to deviate from the modelled values within limits determined by both the measurement and modelling errors. This results in a more realistic uncertainty level of the retrieved AOD. The method is illustrated by both
Structure and selection in an autocatalytic binary polymer model
DEFF Research Database (Denmark)
Tanaka, Shinpei; Fellermann, Harold; Rasmussen, Steen
2014-01-01
a pool of monomers, highly ordered populations with particular sequence patterns are dynamically selected out of a vast number of possible states. The interplay between the selected microscopic sequence patterns and the macroscopic cooperative structures is examined both analytically and in simulation...
Performance Measurement Model for the Supplier Selection Based on AHP
Directory of Open Access Journals (Sweden)
Fabio De Felice
2015-10-01
Full Text Available The performance of the supplier is a crucial factor for the success or failure of any company. Rational and effective decision making in terms of the supplier selection process can help the organization to optimize cost and quality functions. The nature of supplier selection processes is generally complex, especially when the company has a large variety of products and vendors. Over the years, several solutions and methods have emerged for addressing the supplier selection problem (SSP. Experience and studies have shown that there is no best way for evaluating and selecting a specific supplier process, but that it varies from one organization to another. The aim of this research is to demonstrate how a multiple attribute decision making approach can be effectively applied for the supplier selection process.
Genome-wide selection by mixed model ridge regression and extensions based on geostatistical models.
Schulz-Streeck, Torben; Piepho, Hans-Peter
2010-03-31
The success of genome-wide selection (GS) approaches will depend crucially on the availability of efficient and easy-to-use computational tools. Therefore, approaches that can be implemented using mixed models hold particular promise and deserve detailed study. A particular class of mixed models suitable for GS is given by geostatistical mixed models, when genetic distance is treated analogously to spatial distance in geostatistics. We consider various spatial mixed models for use in GS. The analyses presented for the QTL-MAS 2009 dataset pay particular attention to the modelling of residual errors as well as of polygenetic effects. It is shown that geostatistical models are viable alternatives to ridge regression, one of the common approaches to GS. Correlations between genome-wide estimated breeding values and true breeding values were between 0.879 and 0.889. In the example considered, we did not find a large effect of the residual error variance modelling, largely because error variances were very small. A variance components model reflecting the pedigree of the crosses did not provide an improved fit. We conclude that geostatistical models deserve further study as a tool to GS that is easily implemented in a mixed model package.
Learning with Admixture: Modeling, Optimization, and Applications in Population Genetics
DEFF Research Database (Denmark)
Cheng, Jade Yu
2016-01-01
with the big data era. In this PhD dissertation, I present my work on methods and tools developed for two projects, Admixture CoalHMM and Ohana, both of which have been designed to study historical admixture and its influence on population evolution. In Admixture CoalHMM, I make use of full genomic sequences...... geneticists strive to establish working solutions to extract information from massive volumes of biological data. The steep increase in the quantity and quality of genomic data during the past decades provides a unique opportunity but also calls for new and improved algorithms and software to cope...... from a few individuals to perform demographic inference. In Ohana, I use site-independent genomic data from many individuals to analyze individual admixture, to infer population trees, and to identify selection signals. The development of CoalHMM at the Bioinformatics Research Centre at Aarhus...
Lutz, Arthur F.; ter Maat, Herbert W.; Biemans, Hester; Shrestha, Arun B.; Wester, Philippus; Immerzeel, Walter W.
2016-01-01
Climate change impact studies depend on projections of future climate provided by climate models. The number of climate models is large and increasing, yet limitations in computational capacity make it necessary to compromise the number of climate models that can be included in a climate change
Lutz, Arthur F.; Maat, ter Herbert W.; Biemans, Hester; Shrestha, Arun B.; Wester, Philippus; Immerzeel, Walter W.
2016-01-01
Climate change impact studies depend on projections of future climate provided by climate models. The number of climate models is large and increasing, yet limitations in computational capacity make it necessary to compromise the number of climate models that can be included in a climate change
Modeling and Solving the Liner Shipping Service Selection Problem
DEFF Research Database (Denmark)
Karsten, Christian Vad; Balakrishnan, Anant
We address a tactical planning problem, the Liner Shipping Service Selection Problem (LSSSP), facing container shipping companies. Given estimated demand between various ports, the LSSSP entails selecting the best subset of non-simple cyclic sailing routes from a given pool of candidate routes...... requirements and the hop limits to reduce problem size, and describe techniques to accelerate the solution procedure. We present computational results for realistic problem instances from the benchmark suite LINER-LIB....
An Integrated DEMATEL-QFD Model for Medical Supplier Selection
Mehtap Dursun; Zeynep Şener
2014-01-01
Supplier selection is considered as one of the most critical issues encountered by operations and purchasing managers to sharpen the company’s competitive advantage. In this paper, a novel fuzzy multi-criteria group decision making approach integrating quality function deployment (QFD) and decision making trial and evaluation laboratory (DEMATEL) method is proposed for supplier selection. The proposed methodology enables to consider the impacts of inner dependence among supplier assessment cr...
Evaluation of uncertainties in selected environmental dispersion models
International Nuclear Information System (INIS)
Little, C.A.; Miller, C.W.
1979-01-01
Compliance with standards of radiation dose to the general public has necessitated the use of dispersion models to predict radionuclide concentrations in the environment due to releases from nuclear facilities. Because these models are only approximations of reality and because of inherent variations in the input parameters used in these models, their predictions are subject to uncertainty. Quantification of this uncertainty is necessary to assess the adequacy of these models for use in determining compliance with protection standards. This paper characterizes the capabilities of several dispersion models to predict accurately pollutant concentrations in environmental media. Three types of models are discussed: aquatic or surface water transport models, atmospheric transport models, and terrestrial and aquatic food chain models. Using data published primarily by model users, model predictions are compared to observations
Smith, Graham C; Delahay, Richard J; McDonald, Robbie A; Budgey, Richard
2016-01-01
Bovine tuberculosis (bTB) causes substantial economic losses to cattle farmers and taxpayers in the British Isles. Disease management in cattle is complicated by the role of the European badger (Meles meles) as a host of the infection. Proactive, non-selective culling of badgers can reduce the incidence of disease in cattle but may also have negative effects in the area surrounding culls that have been associated with social perturbation of badger populations. The selective removal of infected badgers would, in principle, reduce the number culled, but the effects of selective culling on social perturbation and disease outcomes are unclear. We used an established model to simulate non-selective badger culling, non-selective badger vaccination and a selective trap and vaccinate or remove (TVR) approach to badger management in two distinct areas: South West England and Northern Ireland. TVR was simulated with and without social perturbation in effect. The lower badger density in Northern Ireland caused no qualitative change in the effect of management strategies on badgers, although the absolute number of infected badgers was lower in all cases. However, probably due to differing herd density in Northern Ireland, the simulated badger management strategies caused greater variation in subsequent cattle bTB incidence. Selective culling in the model reduced the number of badgers killed by about 83% but this only led to an overall benefit for cattle TB incidence if there was no social perturbation of badgers. We conclude that the likely benefit of selective culling will be dependent on the social responses of badgers to intervention but that other population factors including badger and cattle density had little effect on the relative benefits of selective culling compared to other methods, and that this may also be the case for disease management in other wild host populations.
Czech Academy of Sciences Publication Activity Database
Šlampová, Andrea; Kubáň, Pavel; Boček, Petr
2014-01-01
Roč. 35, č. 17 (2014), s. 2429-2437 ISSN 0173-0835 R&D Projects: GA ČR(CZ) GA13-05762S Institutional support: RVO:68081715 Keywords : electromembrane extraction * chlorophenols * extraction selectivity Subject RIV: CB - Analytical Chemistry, Separation Impact factor: 3.028, year: 2014
Hogan, Daniel R; Salomon, Joshua A; Canning, David; Hammitt, James K; Zaslavsky, Alan M; Bärnighausen, Till
2012-01-01
Objectives Population-based HIV testing surveys have become central to deriving estimates of national HIV prevalence in sub-Saharan Africa. However, limited participation in these surveys can lead to selection bias. We control for selection bias in national HIV prevalence estimates using a novel approach, which unlike conventional imputation can account for selection on unobserved factors. Methods For 12 Demographic and Health Surveys conducted from 2001 to 2009 (N=138 300), we predict HIV status among those missing a valid HIV test with Heckman-type selection models, which allow for correlation between infection status and participation in survey HIV testing. We compare these estimates with conventional ones and introduce a simulation procedure that incorporates regression model parameter uncertainty into confidence intervals. Results Selection model point estimates of national HIV prevalence were greater than unadjusted estimates for 10 of 12 surveys for men and 11 of 12 surveys for women, and were also greater than the majority of estimates obtained from conventional imputation, with significantly higher HIV prevalence estimates for men in Cote d'Ivoire 2005, Mali 2006 and Zambia 2007. Accounting for selective non-participation yielded 95% confidence intervals around HIV prevalence estimates that are wider than those obtained with conventional imputation by an average factor of 4.5. Conclusions Our analysis indicates that national HIV prevalence estimates for many countries in sub-Saharan African are more uncertain than previously thought, and may be underestimated in several cases, underscoring the need for increasing participation in HIV surveys. Heckman-type selection models should be included in the set of tools used for routine estimation of HIV prevalence. PMID:23172342
2013-04-03
... mathematical modeling methods used in predicting the dispersion of heated effluent in natural water bodies. The... COMMISSION Reporting Procedure for Mathematical Models Selected To Predict Heated Effluent Dispersion in... Mathematical Models Selected to Predict Heated Effluent Dispersion in Natural Water Bodies.'' The guide is...
Akman, Olcay; Hallam, Joshua W.
2010-01-01
We implement genetic algorithm based predictive model building as an alternative to the traditional stepwise regression. We then employ the Information Complexity Measure (ICOMP) as a measure of model fitness instead of the commonly used measure of R-square. Furthermore, we propose some modifications to the genetic algorithm to increase the overall efficiency. PMID:20661297
Directory of Open Access Journals (Sweden)
Olcay Akman
2010-07-01
Full Text Available We implement genetic algorithm based predictive model building as an alternative to the traditional stepwise regression. We then employ the Information Complexity Measure (ICOMP as a measure of model fitness instead of the commonly used measure of R-square. Furthermore, we propose some modifications to the genetic algorithm to increase the overall efficiency.
Achieving pattern uniformity in plasmonic lithography by spatial frequency selection
Liang, Gaofeng; Chen, Xi; Zhao, Qing; Guo, L. Jay
2018-01-01
The effects of the surface roughness of thin films and defects on photomasks are investigated in two representative plasmonic lithography systems: thin silver film-based superlens and multilayer-based hyperbolic metamaterial (HMM). Superlens can replicate arbitrary patterns because of its broad evanescent wave passband, which also makes it inherently vulnerable to the roughness of the thin film and imperfections of the mask. On the other hand, the HMM system has spatial frequency filtering characteristics and its pattern formation is based on interference, producing uniform and stable periodic patterns. In this work, we show that the HMM system is more immune to such imperfections due to its function of spatial frequency selection. The analyses are further verified by an interference lithography system incorporating the photoresist layer as an optical waveguide to improve the aspect ratio of the pattern. It is concluded that a system capable of spatial frequency selection is a powerful method to produce deep-subwavelength periodic patterns with high degree of uniformity and fidelity.
Martin-StPaul, N. K.; Ay, J. S.; Guillemot, J.; Doyen, L.; Leadley, P.
2014-12-01
Species distribution models (SDMs) are widely used to study and predict the outcome of global changes on species. In human dominated ecosystems the presence of a given species is the result of both its ecological suitability and human footprint on nature such as land use choices. Land use choices may thus be responsible for a selection bias in the presence/absence data used in SDM calibration. We present a structural modelling approach (i.e. based on structural equation modelling) that accounts for this selection bias. The new structural species distribution model (SSDM) estimates simultaneously land use choices and species responses to bioclimatic variables. A land use equation based on an econometric model of landowner choices was joined to an equation of species response to bioclimatic variables. SSDM allows the residuals of both equations to be dependent, taking into account the possibility of shared omitted variables and measurement errors. We provide a general description of the statistical theory and a set of applications on forest trees over France using databases of climate and forest inventory at different spatial resolution (from 2km to 8km). We also compared the outputs of the SSDM with outputs of a classical SDM (i.e. Biomod ensemble modelling) in terms of bioclimatic response curves and potential distributions under current climate and climate change scenarios. The shapes of the bioclimatic response curves and the modelled species distribution maps differed markedly between SSDM and classical SDMs, with contrasted patterns according to species and spatial resolutions. The magnitude and directions of these differences were dependent on the correlations between the errors from both equations and were highest for higher spatial resolutions. A first conclusion is that the use of classical SDMs can potentially lead to strong miss-estimation of the actual and future probability of presence modelled. Beyond this selection bias, the SSDM we propose represents
DEFF Research Database (Denmark)
Mikkelsen, Frederik Vissing
Broadly speaking, this thesis is devoted to model selection applied to ordinary dierential equations and risk estimation under model selection. A model selection framework was developed for modelling time course data by ordinary dierential equations. The framework is accompanied by the R software...... eective computational tools for estimating unknown structures in dynamical systems, such as gene regulatory networks, which may be used to predict downstream eects of interventions in the system. A recommended algorithm based on the computational tools is presented and thoroughly tested in various...... simulation studies and applications. The second part of the thesis also concerns model selection, but focuses on risk estimation, i.e., estimating the error of mean estimators involving model selection. An extension of Stein's unbiased risk estimate (SURE), which applies to a class of estimators with model...
Model selection criteria : how to evaluate order restrictions
Kuiper, R.M.
2012-01-01
Researchers often have ideas about the ordering of model parameters. They frequently have one or more theories about the ordering of the group means, in analysis of variance (ANOVA) models, or about the ordering of coefficients corresponding to the predictors, in regression models.A researcher might
The Selection of Turbulence Models for Prediction of Room Airflow
DEFF Research Database (Denmark)
Nielsen, Peter V.
This paper discusses the use of different turbulence models and their advantages in given situations. As an example, it is shown that a simple zero-equation model can be used for the prediction of special situations as flow with a low level of turbulence. A zero-equation model with compensation...
A Four-Step Model for Teaching Selection Interviewing Skills
Kleiman, Lawrence S.; Benek-Rivera, Joan
2010-01-01
The topic of selection interviewing lends itself well to experience-based teaching methods. Instructors often teach this topic by using a two-step process. The first step consists of lecturing students on the basic principles of effective interviewing. During the second step, students apply these principles by role-playing mock interviews with…
Modelling the negative effects of landscape fragmentation on habitat selection
Langevelde, van F.
2015-01-01
Landscape fragmentation constrains movement of animals between habitat patches. Fragmentation may, therefore, limit the possibilities to explore and select the best habitat patches, and some animals may have to cope with low-quality patches due to these movement constraints. If so, these individuals
Selecting Human Error Types for Cognitive Modelling and Simulation
Mioch, T.; Osterloh, J.P.; Javaux, D.
2010-01-01
This paper presents a method that has enabled us to make a selection of error types and error production mechanisms relevant to the HUMAN European project, and discusses the reasons underlying those choices. We claim that this method has the advantage that it is very exhaustive in determining the
RUC at TREC 2014: Select Resources Using Topic Models
2014-11-01
preprocess the data by parsing the pages ( html , txt, doc, xls, ppt, pdf, xml files) into tokens, removing the stopwords listed in the Indri’s...Gravano. Classification-Aware Hidden- Web Text Database Selection. ACM Trans. Inf. Syst. Vol. 26, No. 2, Article 6, April 2008. [8] J. Seo and B. W
The Living Dead: Transformative Experiences in Modelling Natural Selection
Petersen, Morten Rask
2017-01-01
This study considers how students change their coherent conceptual understanding of natural selection through a hands-on simulation. The results show that most students change their understanding. In addition, some students also underwent a transformative experience and used their new knowledge in a leisure time activity. These transformative…
Directory of Open Access Journals (Sweden)
Mark N Read
2016-09-01
Full Text Available The advent of two-photon microscopy now reveals unprecedented, detailed spatio-temporal data on cellular motility and interactions in vivo. Understanding cellular motility patterns is key to gaining insight into the development and possible manipulation of the immune response. Computational simulation has become an established technique for understanding immune processes and evaluating hypotheses in the context of experimental data, and there is clear scope to integrate microscopy-informed motility dynamics. However, determining which motility model best reflects in vivo motility is non-trivial: 3D motility is an intricate process requiring several metrics to characterize. This complicates model selection and parameterization, which must be performed against several metrics simultaneously. Here we evaluate Brownian motion, Lévy walk and several correlated random walks (CRWs against the motility dynamics of neutrophils and lymph node T cells under inflammatory conditions by simultaneously considering cellular translational and turn speeds, and meandering indices. Heterogeneous cells exhibiting a continuum of inherent translational speeds and directionalities comprise both datasets, a feature significantly improving capture of in vivo motility when simulated as a CRW. Furthermore, translational and turn speeds are inversely correlated, and the corresponding CRW simulation again improves capture of our in vivo data, albeit to a lesser extent. In contrast, Brownian motion poorly reflects our data. Lévy walk is competitive in capturing some aspects of neutrophil motility, but T cell directional persistence only, therein highlighting the importance of evaluating models against several motility metrics simultaneously. This we achieve through novel application of multi-objective optimization, wherein each model is independently implemented and then parameterized to identify optimal trade-offs in performance against each metric. The resultant Pareto
Model selection for integrated pest management with stochasticity.
Akman, Olcay; Comar, Timothy D; Hrozencik, Daniel
2018-04-07
In Song and Xiang (2006), an integrated pest management model with periodically varying climatic conditions was introduced. In order to address a wider range of environmental effects, the authors here have embarked upon a series of studies resulting in a more flexible modeling approach. In Akman et al. (2013), the impact of randomly changing environmental conditions is examined by incorporating stochasticity into the birth pulse of the prey species. In Akman et al. (2014), the authors introduce a class of models via a mixture of two birth-pulse terms and determined conditions for the global and local asymptotic stability of the pest eradication solution. With this work, the authors unify the stochastic and mixture model components to create further flexibility in modeling the impacts of random environmental changes on an integrated pest management system. In particular, we first determine the conditions under which solutions of our deterministic mixture model are permanent. We then analyze the stochastic model to find the optimal value of the mixing parameter that minimizes the variance in the efficacy of the pesticide. Additionally, we perform a sensitivity analysis to show that the corresponding pesticide efficacy determined by this optimization technique is indeed robust. Through numerical simulations we show that permanence can be preserved in our stochastic model. Our study of the stochastic version of the model indicates that our results on the deterministic model provide informative conclusions about the behavior of the stochastic model. Copyright © 2017 Elsevier Ltd. All rights reserved.
A hidden Markov model approach for determining expression from genomic tiling micro arrays
DEFF Research Database (Denmark)
Terkelsen, Kasper Munch; Gardner, P. P.; Arctander, Peter
2006-01-01
Background Genomic tiling micro arrays have great potential for identifying previously undiscovered coding as well as non-coding transcription. To-date, however, analyses of these data have been performed in an ad hoc fashion. Results We present a probabilistic procedure, ExpressHMM, that adaptiv......Background Genomic tiling micro arrays have great potential for identifying previously undiscovered coding as well as non-coding transcription. To-date, however, analyses of these data have been performed in an ad hoc fashion. Results We present a probabilistic procedure, Express......HMM, that adaptively models tiling data prior to predicting expression on genomic sequence. A hidden Markov model (HMM) is used to model the distributions of tiling array probe scores in expressed and non-expressed regions. The HMM is trained on sets of probes mapped to regions of annotated expression and non......-expression. Subsequently, prediction of transcribed fragments is made on tiled genomic sequence. The prediction is accompanied by an expression probability curve for visual inspection of the supporting evidence. We test ExpressHMM on data from the Cheng et al. (2005) tiling array experiments on ten Human chromosomes [1...
Selected Aspects of Computer Modeling of Reinforced Concrete Structures
Directory of Open Access Journals (Sweden)
Szczecina M.
2016-03-01
Full Text Available The paper presents some important aspects concerning material constants of concrete and stages of modeling of reinforced concrete structures. The problems taken into account are: a choice of proper material model for concrete, establishing of compressive and tensile behavior of concrete and establishing the values of dilation angle, fracture energy and relaxation time for concrete. Proper values of material constants are fixed in simple compression and tension tests. The effectiveness and correctness of applied model is checked on the example of reinforced concrete frame corners under opening bending moment. Calculations are performed in Abaqus software using Concrete Damaged Plasticity model of concrete.
Santing, R.E; de Boer, J; Rohof, A.A B; van der Zee, N.M; Zaagsma, Hans
2001-01-01
In a guinea pig model of allergic asthma, we investigated the effects of the selective phosphodiesterase inhibitors rolipram (phosphodiesterase 4-selective), Org 9935 (phosphodiesterase 3-selective) and Org 20241 (dual phosphodiesterase 4/phosphodiesterase 3-selective), administered by aerosol
A model selection support system for numerical simulations of nuclear thermal-hydraulics
International Nuclear Information System (INIS)
Gofuku, Akio; Shimizu, Kenji; Sugano, Keiji; Yoshikawa, Hidekazu; Wakabayashi, Jiro
1990-01-01
In order to execute efficiently a dynamic simulation of a large-scaled engineering system such as a nuclear power plant, it is necessary to develop intelligent simulation support system for all phases of the simulation. This study is concerned with the intelligent support for the program development phase and is engaged in the adequate model selection support method by applying AI (Artificial Intelligence) techniques to execute a simulation consistent with its purpose and conditions. A proto-type expert system to support the model selection for numerical simulations of nuclear thermal-hydraulics in the case of cold leg small break loss-of-coolant accident of PWR plant is now under development on a personal computer. The steps to support the selection of both fluid model and constitutive equations for the drift flux model have been developed. Several cases of model selection were carried out and reasonable model selection results were obtained. (author)
Schwaab, Douglas G.
1991-01-01
A mathematical programing model is presented to optimize the selection of Orbital Replacement Unit on-orbit spares for the Space Station. The model maximizes system availability under the constraints of logistics resupply-cargo weight and volume allocations.
Random forest (RF) modeling has emerged as an important statistical learning method in ecology due to its exceptional predictive performance. However, for large and complex ecological datasets there is limited guidance on variable selection methods for RF modeling. Typically, e...
Optimizing warehouse logistics operations through site selection models : Istanbul, Turkey
Erdemir, Ugur
2003-01-01
Approved for public release; distribution is unlimited This thesis makes a cost benefit analysis of relocating the outdated and earthquake damaged supply distribution center of the Turkish Navy. Given the dynamic environment surrounding the military operations, logistic sustainability requirements, rapid information technology developments, and budget-constrained Turkish DoD acquisition environment, the site selection of a supply distribution center is critical to the future operations and...
River water quality model no. 1 (RWQM1): III. Biochemical submodel selection
DEFF Research Database (Denmark)
Vanrolleghem, P.; Borchardt, D.; Henze, Mogens
2001-01-01
The new River Water Quality Model no.1 introduced in the two accompanying papers by Shanahan et al. and Reichert et al. is comprehensive. Shanahan et al. introduced a six-step decision procedure to select the necessary model features for a certain application. This paper specifically addresses one...... of these steps, i.e. the selection of submodels of the comprehensive biochemical conversion model introduced in Reichert et al. Specific conditions for inclusion of one or the other conversion process or model component are introduced, as are some general rules that can support the selection. Examples...... of simplified models are presented....
Default Bayes Factors for Model Selection in Regression
Rouder, Jeffrey N.; Morey, Richard D.
2012-01-01
In this article, we present a Bayes factor solution for inference in multiple regression. Bayes factors are principled measures of the relative evidence from data for various models or positions, including models that embed null hypotheses. In this regard, they may be used to state positive evidence for a lack of an effect, which is not possible…
The analysis of the capacity of the selected measures of decision-making models in companies
Helena Kościelniak; Beata Skowron-Grabowska; Sylwia Łęgowik-Świącik; Małgorzata Łęgowik-Małolepsza
2015-01-01
The paper aims at the analysis of the information capacity of selected instruments of the assessment of decision-making models in the analyzed companies. In the paper there are presented the idea and concepts of decision-making models. There have been discussed the selected instruments of the assessment of decision-making models in enterprises. In the final part of the paper there has been held the quantification of decision- making models in the investigated cement industry companies. To mee...
Varying Coefficient Panel Data Model in the Presence of Endogenous Selectivity and Fixed Effects
Malikov, Emir; Kumbhakar, Subal C.; Sun, Yiguo
2013-01-01
This paper considers a flexible panel data sample selection model in which (i) the outcome equation is permitted to take a semiparametric, varying coefficient form to capture potential parameter heterogeneity in the relationship of interest, (ii) both the outcome and (parametric) selection equations contain unobserved fixed effects and (iii) selection is generalized to a polychotomous case. We propose a two-stage estimator. Given consistent parameter estimates from the selection equation obta...
Meuwissen, Theo H E; Indahl, Ulf G; Ødegård, Jørgen
2017-12-27
Non-linear Bayesian genomic prediction models such as BayesA/B/C/R involve iteration and mostly Markov chain Monte Carlo (MCMC) algorithms, which are computationally expensive, especially when whole-genome sequence (WGS) data are analyzed. Singular value decomposition (SVD) of the genotype matrix can facilitate genomic prediction in large datasets, and can be used to estimate marker effects and their prediction error variances (PEV) in a computationally efficient manner. Here, we developed, implemented, and evaluated a direct, non-iterative method for the estimation of marker effects for the BayesC genomic prediction model. The BayesC model assumes a priori that markers have normally distributed effects with probability [Formula: see text] and no effect with probability (1 - [Formula: see text]). Marker effects and their PEV are estimated by using SVD and the posterior probability of the marker having a non-zero effect is calculated. These posterior probabilities are used to obtain marker-specific effect variances, which are subsequently used to approximate BayesC estimates of marker effects in a linear model. A computer simulation study was conducted to compare alternative genomic prediction methods, where a single reference generation was used to estimate marker effects, which were subsequently used for 10 generations of forward prediction, for which accuracies were evaluated. SVD-based posterior probabilities of markers having non-zero effects were generally lower than MCMC-based posterior probabilities, but for some regions the opposite occurred, resulting in clear signals for QTL-rich regions. The accuracies of breeding values estimated using SVD- and MCMC-based BayesC analyses were similar across the 10 generations of forward prediction. For an intermediate number of generations (2 to 5) of forward prediction, accuracies obtained with the BayesC model tended to be slightly higher than accuracies obtained using the best linear unbiased prediction of SNP
Evolving the structure of hidden Markov Models
DEFF Research Database (Denmark)
won, K. J.; Prugel-Bennett, A.; Krogh, A.
2006-01-01
A genetic algorithm (GA) is proposed for finding the structure of hidden Markov Models (HMMs) used for biological sequence analysis. The GA is designed to preserve biologically meaningful building blocks. The search through the space of HMM structures is combined with optimization of the emission...
Identification of landscape features influencing gene flow: How useful are habitat selection models?
Gretchen H. Roffler; Michael K. Schwartz; Kristine Pilgrim; Sandra L. Talbot; George K. Sage; Layne G. Adams; Gordon Luikart
2016-01-01
Understanding how dispersal patterns are influenced by landscape heterogeneity is critical for modeling species connectivity. Resource selection function (RSF) models are increasingly used in landscape genetics approaches. However, because the ecological factors that drive habitat selection may be different from those influencing dispersal and gene flow, it is...
An Analysis and Implementation of the Hidden Markov Model to Technology Stock Prediction
Directory of Open Access Journals (Sweden)
Nguyet Nguyen
2017-11-01
Full Text Available Future stock prices depend on many internal and external factors that are not easy to evaluate. In this paper, we use the Hidden Markov Model, (HMM, to predict a daily stock price of three active trading stocks: Apple, Google, and Facebook, based on their historical data. We first use the Akaike information criterion (AIC and Bayesian information criterion (BIC to choose the numbers of states from HMM. We then use the models to predict close prices of these three stocks using both single observation data and multiple observation data. Finally, we use the predictions as signals for trading these stocks. The criteria tests’ results showed that HMM with two states worked the best among two, three and four states for the three stocks. Our results also demonstrate that the HMM outperformed the naïve method in forecasting stock prices. The results also showed that active traders using HMM got a higher return than using the naïve forecast for Facebook and Google stocks. The stock price prediction method has a significant impact on stock trading and derivative hedging.
Optimal covariance selection for estimation using graphical models
Vichik, Sergey; Oshman, Yaakov
2011-01-01
We consider a problem encountered when trying to estimate a Gaussian random field using a distributed estimation approach based on Gaussian graphical models. Because of constraints imposed by estimation tools used in Gaussian graphical models, the a priori covariance of the random field is constrained to embed conditional independence constraints among a significant number of variables. The problem is, then: given the (unconstrained) a priori covariance of the random field, and the conditiona...
Fuzzy Multicriteria Model for Selection of Vibration Technology
Directory of Open Access Journals (Sweden)
María Carmen Carnero
2016-01-01
Full Text Available The benefits of applying the vibration analysis program are well known and have been so for decades. A large number of contributions have been produced discussing new diagnostic, signal treatment, technical parameter analysis, and prognosis techniques. However, to obtain the expected benefits from a vibration analysis program, it is necessary to choose the instrumentation which guarantees the best results. Despite its importance, in the literature, there are no models to assist in taking this decision. This research describes an objective model using Fuzzy Analytic Hierarchy Process (FAHP to make a choice of the most suitable technology among portable vibration analysers. The aim is to create an easy-to-use model for processing, manufacturing, services, and research organizations, to guarantee adequate decision-making in the choice of vibration analysis technology. The model described recognises that judgements are often based on ambiguous, imprecise, or inadequate information that cannot provide precise values. The model incorporates judgements from several decision-makers who are experts in the field of vibration analysis, maintenance, and electronic devices. The model has been applied to a Health Care Organization.
Multi-Criteria Decision Making For Determining A Simple Model of Supplier Selection
Harwati
2017-06-01
Supplier selection is a decision with many criteria. Supplier selection model usually involves more than five main criteria and more than 10 sub-criteria. In fact many model includes more than 20 criteria. Too many criteria involved in supplier selection models sometimes make it difficult to apply in many companies. This research focuses on designing supplier selection that easy and simple to be applied in the company. Analytical Hierarchy Process (AHP) is used to weighting criteria. The analysis results there are four criteria that are easy and simple can be used to select suppliers: Price (weight 0.4) shipment (weight 0.3), quality (weight 0.2) and services (weight 0.1). A real case simulation shows that simple model provides the same decision with a more complex model.
Selecting a climate model subset to optimise key ensemble properties
Directory of Open Access Journals (Sweden)
N. Herger
2018-02-01
Full Text Available End users studying impacts and risks caused by human-induced climate change are often presented with large multi-model ensembles of climate projections whose composition and size are arbitrarily determined. An efficient and versatile method that finds a subset which maintains certain key properties from the full ensemble is needed, but very little work has been done in this area. Therefore, users typically make their own somewhat subjective subset choices and commonly use the equally weighted model mean as a best estimate. However, different climate model simulations cannot necessarily be regarded as independent estimates due to the presence of duplicated code and shared development history. Here, we present an efficient and flexible tool that makes better use of the ensemble as a whole by finding a subset with improved mean performance compared to the multi-model mean while at the same time maintaining the spread and addressing the problem of model interdependence. Out-of-sample skill and reliability are demonstrated using model-as-truth experiments. This approach is illustrated with one set of optimisation criteria but we also highlight the flexibility of cost functions, depending on the focus of different users. The technique is useful for a range of applications that, for example, minimise present-day bias to obtain an accurate ensemble mean, reduce dependence in ensemble spread, maximise future spread, ensure good performance of individual models in an ensemble, reduce the ensemble size while maintaining important ensemble characteristics, or optimise several of these at the same time. As in any calibration exercise, the final ensemble is sensitive to the metric, observational product, and pre-processing steps used.
Selecting a climate model subset to optimise key ensemble properties
Herger, Nadja; Abramowitz, Gab; Knutti, Reto; Angélil, Oliver; Lehmann, Karsten; Sanderson, Benjamin M.
2018-02-01
End users studying impacts and risks caused by human-induced climate change are often presented with large multi-model ensembles of climate projections whose composition and size are arbitrarily determined. An efficient and versatile method that finds a subset which maintains certain key properties from the full ensemble is needed, but very little work has been done in this area. Therefore, users typically make their own somewhat subjective subset choices and commonly use the equally weighted model mean as a best estimate. However, different climate model simulations cannot necessarily be regarded as independent estimates due to the presence of duplicated code and shared development history. Here, we present an efficient and flexible tool that makes better use of the ensemble as a whole by finding a subset with improved mean performance compared to the multi-model mean while at the same time maintaining the spread and addressing the problem of model interdependence. Out-of-sample skill and reliability are demonstrated using model-as-truth experiments. This approach is illustrated with one set of optimisation criteria but we also highlight the flexibility of cost functions, depending on the focus of different users. The technique is useful for a range of applications that, for example, minimise present-day bias to obtain an accurate ensemble mean, reduce dependence in ensemble spread, maximise future spread, ensure good performance of individual models in an ensemble, reduce the ensemble size while maintaining important ensemble characteristics, or optimise several of these at the same time. As in any calibration exercise, the final ensemble is sensitive to the metric, observational product, and pre-processing steps used.
Selected developments and applications of Leontief models in industrial ecology
International Nuclear Information System (INIS)
Stroemman, Anders Hammer
2005-01-01
Thesis Outline: This thesis investigates issues of environmental repercussions on processes of three spatial scales; a single process plant, a regional value chain and the global economy. The first paper investigates environmental repercussions caused by a single process plant using an open Leontief model with combined physical and monetary units in what is commonly referred to as a hybrid life cycle model. Physical capital requirements are treated as any other good. Resources and environmental stressors, thousands in total, are accounted for and assessed by aggregation using standard life cycle impact assessment methods. The second paper presents a methodology for establishing and combining input-output matrices and life-cycle inventories in a hybrid life cycle inventory. Information contained within different requirements matrices are combined and issues of double counting that arise are addressed and methods for eliminating these are developed and presented. The third paper is an extension of the first paper. Here the system analyzed is increased from a single plant and component in the production network to a series of nodes, constituting a value chain. The hybrid framework proposed in paper two is applied to analyze the use of natural gas, methanol and hydrogen as transportation fuels. The fourth paper presents the development of a World Trade Model with Bilateral Trade, an extension of the World Trade Model (Duchin, 2005). The model is based on comparative advantage and is formulated as a linear program. It endogenously determines the regional output of sectors and bilateral trade flows between regions. The model may be considered a Leontief substitution model where substitution of production is allowed between regions. The primal objective of the model requires the minimization of global factor costs. The fifth paper demonstrates how the World Trade Model with Bilateral Trade can be applied to address questions relevant for industrial ecology. The model is
Selection of References in Wind Turbine Model Predictive Control Design
DEFF Research Database (Denmark)
Odgaard, Peter Fogh; Hovgaard, Tobias
2015-01-01
a model predictive controller for a wind turbine. One of the important aspects for a tracking control problem is how to setup the optimal reference tracking problem, as it might be relevant to track, e.g., the three concurrent references: optimal pitch angle, optimal rotational speed, and optimal power....... The importance if the individual references differ depending in particular on the wind speed. In this paper we investigate the performance of a reference tracking model predictive controller with two different setups of the used optimal reference signals. The controllers are evaluated using an industrial high...
Selection of antioxidants against ovarian oxidative stress in mouse model.
Li, Bojiang; Weng, Qiannan; Liu, Zequn; Shen, Ming; Zhang, Jiaqing; Wu, Wangjun; Liu, Honglin
2017-12-01
Oxidative stress (OS) plays an important role in the process of ovarian granulosa cell apoptosis and follicular atresia. The aim of this study was to select antioxidant against OS in ovary tissue. Firstly, we chose the six antioxidants and analyzed the reactive oxygen species (ROS) level in the ovary tissue. The results showed that proanthocyanidins, gallic acid, curcumin, and carotene decrease the ROS level compared with control group. We further demonstrated that both proanthocyanidins and gallic acid increase the antioxidant enzymes activity. Moreover, change in the ROS level was not observed in proanthocyanidins and gallic acid group of brain, liver, spleen, and kidney tissues. Finally, we found that proanthocyanidins and gallic acid inhibit pro-apoptotic genes expression in granulosa cells. Taken together, proanthocyanidins and gallic acid may be the most acceptable and optimal antioxidants specifically against ovarian OS and also may be involved in the inhibition of granulosa cells apoptosis in mouse ovary. © 2017 Wiley Periodicals, Inc.
An Optimization Model For Strategy Decision Support to Select Kind of CPO’s Ship
Suaibah Nst, Siti; Nababan, Esther; Mawengkang, Herman
2018-01-01
The selection of marine transport for the distribution of crude palm oil (CPO) is one of strategy that can be considered in reducing cost of transport. The cost of CPO’s transport from one area to CPO’s factory located at the port of destination may affect the level of CPO’s prices and the number of demands. In order to maintain the availability of CPO a strategy is required to minimize the cost of transporting. In this study, the strategy used to select kind of charter ships as barge or chemical tanker. This study aims to determine an optimization model for strategy decision support in selecting kind of CPO’s ship by minimizing costs of transport. The select of ship was done randomly, so that two-stage stochastic programming model was used to select the kind of ship. Model can help decision makers to select either barge or chemical tanker to distribute CPO.
Hidden Markov Model for quantitative prediction of snowfall
Indian Academy of Sciences (India)
A Hidden Markov Model (HMM) has been developed for prediction of quantitative snowfall in Pir-Panjal and Great Himalayan mountain ranges of Indian Himalaya. The model predicts snowfall for two days in advance using daily recorded nine meteorological variables of past 20 winters from 1992–2012. There are six ...
A Hidden Markov Model for avalanche forecasting on Chowkibal ...
Indian Academy of Sciences (India)
) in J&K, India. Table 1. Summary statistics of the data used in the model. Average ... attempt to introduce snow pack information into the model. 3. Methodology. The HMM consists of two parts: • an unobserved state sequence S1, S2,...,Sn of.
Hidden Markov Model for quantitative prediction of snowfall and ...
Indian Academy of Sciences (India)
A Hidden Markov Model (HMM) has been developed for prediction of quantitative snowfall in Pir-Panjal and Great Himalayan mountain ranges of Indian Himalaya. The model predicts snowfall for two days in advance using daily recorded nine meteorological variables of past 20 winters from 1992–2012. There are six ...
Optimization and evaluation of probabilistic-logic sequence models
DEFF Research Database (Denmark)
Christiansen, Henning; Lassen, Ole Torp
Analysis of biological sequence data demands more and more sophisticated and fine-grained models, but these in turn introduce hard computational problems. A class of probabilistic-logic models is considered, which increases the expressibility from HMM's and SCFG's regular and context-free languages...
Broken selection rule in the quantum Rabi model
Forn Diaz, P.; Gonzalez-Romero, E; Harmans, C.J.P.M.; Solano, E; Mooij, J.E.
2016-01-01
Understanding the interaction between light and matter is very relevant for fundamental studies of quantum electrodynamics and for the development of quantum technologies. The quantum Rabi model captures the physics of a single atom interacting with a single photon at all regimes of coupling
Parameter Estimation and Model Selection for Mixtures of Truncated Exponentials
DEFF Research Database (Denmark)
Langseth, Helge; Nielsen, Thomas Dyhre; Rumí, Rafael
2010-01-01
Bayesian networks with mixtures of truncated exponentials (MTEs) support efficient inference algorithms and provide a flexible way of modeling hybrid domains (domains containing both discrete and continuous variables). On the other hand, estimating an MTE from data has turned out to be a difficult...
The Applicability of Selected Evaluation Models to Evolving Investigative Designs.
Smith, Nick L.; Hauer, Diane M.
1990-01-01
Ten evaluation models are examined in terms of their applicability to investigative, emergent design programs: Stake's portrayal, Wolf's adversary, Patton's utilization, Guba's investigative journalism, Scriven's goal-free, Scriven's modus operandi, Eisner's connoisseurial, Stufflebeam's CIPP, Tyler's objective based, and Levin's cost…
Modeling Selected Climatic Variables in Ibadan, Oyo State, Nigeria ...
African Journals Online (AJOL)
PROF. O. E. OSUAGWU
2013-09-01
Sep 1, 2013 ... The aim of this study was fitting the modified generalized burr density function to total rainfall and temperature data obtained from the meteorological unit in the Department of. Environmental Modelling and Management of the Forestry Research Institute of Nigeria. (FRIN) in Ibadan, Oyo State, Nigeria.
Model Selection for Nondestructive Quantification of Fruit Growth in Pepper
Wubs, A.M.; Ma, Y.T.; Heuvelink, E.; Hemerik, L.; Marcelis, L.F.M.
2012-01-01
Quantifying fruit growth can be desirable for several purposes (e.g., prediction of fruit yield and size, or for the use in crop simulation models). The goal of this article was to determine the best sigmoid function to describe fruit growth of pepper (Capsicum annuum) from nondestructive fruit
The Optimal Portfolio Selection Model under g-Expectation
Directory of Open Access Journals (Sweden)
Li Li
2014-01-01
complicated and sophisticated, the optimal solution turns out to be surprisingly simple, the payoff of a portfolio of two binary claims. Also I give the economic meaning of my model and the comparison with that one in the work of Jin and Zhou, 2008.
Selecting Tools to Model Integer and Binomial Multiplication
Pratt, Sarah Smitherman; Eddy, Colleen M.
2017-01-01
Mathematics teachers frequently provide concrete manipulatives to students during instruction; however, the rationale for using certain manipulatives in conjunction with concepts may not be explored. This article focuses on area models that are currently used in classrooms to provide concrete examples of integer and binomial multiplication. The…
An individual-level selection model for the apparent altruism ...
Indian Academy of Sciences (India)
Amotz Zahavi
2018-02-16
Feb 16, 2018 ... remain solitary when the rest have completed aggregation. Their response to starvation (apparently) is not to become part of an aggregate, but instead to take a chance on a fresh source of food appearing quickly. Modelling shows that given the right environmental conditions, this can work. (Tarnita et al.
Process chain modeling and selection in an additive manufacturing context
DEFF Research Database (Denmark)
Thompson, Mary Kathryn; Stolfi, Alessandro; Mischkot, Michael
2016-01-01
This paper introduces a new two-dimensional approach to modeling manufacturing process chains. This approach is used to consider the role of additive manufacturing technologies in process chains for a part with micro scale features and no internal geometry. It is shown that additive manufacturing...... evolving fields like additive manufacturing....
Selected Constitutive Models for Simulating the Hygromechanical Response of Wood
DEFF Research Database (Denmark)
Frandsen, Henrik Lund
The present thesis is a compilation of papers. Three of the papers, I , VI and VII, are published in this thesis only, i.e., an introductory paper and two so-called discussion papers. The papers II, III and V have been published in the international journal, Holzforschung. Paper IV is a conferenc...... paper presented at the 19th Nordic Seminar on Computational Mechanics, Lund, Sweden, 2006. Paper I: The theories for the phenomena leading to hygromechanical response of wood relate to the orthotropic cellular structure and the hydrophilic and hydrophobic polymers constituting the cells...... of wood as a state in the sorption hysteresis space, which is independent of the condition of water vapor in the lumens. Two approaches are developed and tested by implementation into commercial software. Paper VI: The temperature dependencies of the hysteretic multi-Fickian moisture transport model...... are discussed. The constitutive moisture transport models are coupled with a heat transport model yielding terms that describe the so-called Dufour and Sorret effects, however, with multiple phases and hysteresis included. Paper VII: In this paper the modeling of transverse couplings in creep of wood...
A BAYESIAN NONPARAMETRIC MIXTURE MODEL FOR SELECTING GENES AND GENE SUBNETWORKS.
Zhao, Yize; Kang, Jian; Yu, Tianwei
2014-06-01
It is very challenging to select informative features from tens of thousands of measured features in high-throughput data analysis. Recently, several parametric/regression models have been developed utilizing the gene network information to select genes or pathways strongly associated with a clinical/biological outcome. Alternatively, in this paper, we propose a nonparametric Bayesian model for gene selection incorporating network information. In addition to identifying genes that have a strong association with a clinical outcome, our model can select genes with particular expressional behavior, in which case the regression models are not directly applicable. We show that our proposed model is equivalent to an infinity mixture model for which we develop a posterior computation algorithm based on Markov chain Monte Carlo (MCMC) methods. We also propose two fast computing algorithms that approximate the posterior simulation with good accuracy but relatively low computational cost. We illustrate our methods on simulation studies and the analysis of Spellman yeast cell cycle microarray data.
Evaluation and comparison of alternative fleet-level selective maintenance models
International Nuclear Information System (INIS)
Schneider, Kellie; Richard Cassady, C.
2015-01-01
Fleet-level selective maintenance refers to the process of identifying the subset of maintenance actions to perform on a fleet of repairable systems when the maintenance resources allocated to the fleet are insufficient for performing all desirable maintenance actions. The original fleet-level selective maintenance model is designed to maximize the probability that all missions in a future set are completed successfully. We extend this model in several ways. First, we consider a cost-based optimization model and show that a special case of this model maximizes the expected value of the number of successful missions in the future set. We also consider the situation in which one or more of the future missions may be canceled. These models and the original fleet-level selective maintenance optimization models are nonlinear. Therefore, we also consider an alternative model in which the objective function can be linearized. We show that the alternative model is a good approximation to the other models. - Highlights: • Investigate nonlinear fleet-level selective maintenance optimization models. • A cost based model is used to maximize the expected number of successful missions. • Another model is allowed to cancel missions if reliability is sufficiently low. • An alternative model has an objective function that can be linearized. • We show that the alternative model is a good approximation to the other models
A Two-Channel Training Algorithm for Hidden Markov Model and Its Application to Lip Reading
Directory of Open Access Journals (Sweden)
Foo Say Wei
2005-01-01
Full Text Available Hidden Markov model (HMM has been a popular mathematical approach for sequence classification such as speech recognition since 1980s. In this paper, a novel two-channel training strategy is proposed for discriminative training of HMM. For the proposed training strategy, a novel separable-distance function that measures the difference between a pair of training samples is adopted as the criterion function. The symbol emission matrix of an HMM is split into two channels: a static channel to maintain the validity of the HMM and a dynamic channel that is modified to maximize the separable distance. The parameters of the two-channel HMM are estimated by iterative application of expectation-maximization (EM operations. As an example of the application of the novel approach, a hierarchical speaker-dependent visual speech recognition system is trained using the two-channel HMMs. Results of experiments on identifying a group of confusable visemes indicate that the proposed approach is able to increase the recognition accuracy by an average of 20% compared with the conventional HMMs that are trained with the Baum-Welch estimation.
Model-supported selection of distribution coefficients for performance assessment
International Nuclear Information System (INIS)
Ochs, M.; Lothenbach, B.; Shibata, Hirokazu; Yui, Mikazu
1999-01-01
A thermodynamic speciation/sorption model is used to illustrate typical problems encountered in the extrapolation of batch-type K d values to repository conditions. For different bentonite-groundwater systems, the composition of the corresponding equilibrium solutions and the surface speciation of the bentonite is calculated by treating simultaneously solution equilibria of soluble components of the bentonite as well as ion exchange and acid/base reactions at the bentonite surface. K d values for Cs, Ra, and Ni are calculated by implementing the appropriate ion exchange and surface complexation equilibria in the bentonite model. Based on this approach, hypothetical batch experiments are contrasted with expected conditions in compacted backfill. For each of these scenarios, the variation of K d values as a function of groundwater composition is illustrated for Cs, Ra, and Ni. The applicability of measured, batch-type K d values to repository conditions is discussed. (author)
Selected topics in phenomenology of the standard model
International Nuclear Information System (INIS)
Roberts, R.G.
1992-01-01
We begin with the structure of the proton which is revealed through deep inelastic scattering of nucleons by electron/muon or neutrino scattering off nucleons. The quark parton model is described which leads on to the interaction of quarks and gluons - quantum chromodynamics (QCD). From this parton distributions can be extracted and then fed into the quark parton description of hadron-hadron collisions. In this way we analyse large p T jet production, prompt photon production and dilepton, W and Z production (Drell-Yan mechanism), ending with a study of heavy quark production. W and Z physics is then discussed. The various definitions at the tree level of sin 2 θ w are listed and then the radiative corrections to these are briefly considered. The data from European Large Electron-Positron storage rings (LEP) then allow limits to be set on the mass of the top quark and the Higgs via these corrections. Standard model predictions for the various Z widths are compared with the latest LEP data. Electroweak effects in e + e - scattering are discussed together with the extraction of the various vector and axial-vector couplings involved. We return to QCD when the production of jets in e + e - is studied. Both the LEP and lower energy data are able to give quantitative estimates of the strong coupling α s and the consistency of the various estimates and those from other QCD processes are discussed. The value of α s (M z ) actually plays an important role in setting the scale of the possible supersymmetry (SUSY) physics beyond the standard model. Finally the subject of quark mixing is addressed. How the the values of the various CKM matrix elements are derived is discussed together with a very brief look at the charge-parity (CP) violation and how the standard model is standing up to the latest measurements of ε'/ε. (Author)
Barbie selected for QM1 as role models change
Eitelberg, Mark J.
1991-01-01
An article discussing the changing role models and attitudes of younf women as reflected in the introduction of Army Barbie, Air Force Barbie and Navy Barbie dolls for children. The author's commentary discusses the differences in each service's approach to the dolls, and their importance as part of the culture, as much an American institution as a toy. The author notes that the manufacturer's willingness to accept the attitude that the military is an acceptable career choice for young women...
A New Approach to Model Verification, Falsification and Selection
Directory of Open Access Journals (Sweden)
Andrew J. Buck
2015-06-01
Full Text Available This paper shows that a qualitative analysis, i.e., an assessment of the consistency of a hypothesized sign pattern for structural arrays with the sign pattern of the estimated reduced form, can always provide decisive insight into a model’s validity both in general and compared to other models. Qualitative analysis can show that it is impossible for some models to have generated the data used to estimate the reduced form, even though standard specification tests might show the model to be adequate. A partially specified structural hypothesis can be falsified by estimating as few as one reduced form equation. Zero restrictions in the structure can themselves be falsified. It is further shown how the information content of the hypothesized structural sign patterns can be measured using a commonly applied concept of statistical entropy. The lower the hypothesized structural sign pattern’s entropy, the more a priori information it proposes about the sign pattern of the estimated reduced form. As an hypothesized structural sign pattern has a lower entropy, it is more subject to type 1 error and less subject to type 2 error. Three cases illustrate the approach taken here.
A Model for Service Life Control of Selected Device Systems
Directory of Open Access Journals (Sweden)
Zieja Mariusz
2014-04-01
Full Text Available This paper presents a way of determining distribution of limit state exceedence time by a diagnostic parameter which determines accuracy of maintaining zero state. For calculations it was assumed that the diagnostic parameter is deviation from nominal value (zero state. Change of deviation value occurs as a result of destructive processes which occur during service. For estimation of deviation increasing rate in probabilistic sense, was used a difference equation from which, after transformation, Fokker-Planck differential equation was obtained [4, 11]. A particular solution of the equation is deviation increasing rate density function which was used for determining exceedance probability of limit state. The so-determined probability was then used to determine density function of limit state exceedance time, by increasing deviation. Having at disposal the density function of limit state exceedance time one determined service life of a system of maladjustment. In the end, a numerical example based on operational data of selected aircraft [weapon] sights was presented. The elaborated method can be also applied to determining residual life of shipboard devices whose technical state is determined on the basis of analysis of values of diagnostic parameters.
Probabilistic wind power forecasting with online model selection and warped gaussian process
International Nuclear Information System (INIS)
Kou, Peng; Liang, Deliang; Gao, Feng; Gao, Lin
2014-01-01
Highlights: • A new online ensemble model for the probabilistic wind power forecasting. • Quantifying the non-Gaussian uncertainties in wind power. • Online model selection that tracks the time-varying characteristic of wind generation. • Dynamically altering the input features. • Recursive update of base models. - Abstract: Based on the online model selection and the warped Gaussian process (WGP), this paper presents an ensemble model for the probabilistic wind power forecasting. This model provides the non-Gaussian predictive distributions, which quantify the non-Gaussian uncertainties associated with wind power. In order to follow the time-varying characteristics of wind generation, multiple time dependent base forecasting models and an online model selection strategy are established, thus adaptively selecting the most probable base model for each prediction. WGP is employed as the base model, which handles the non-Gaussian uncertainties in wind power series. Furthermore, a regime switch strategy is designed to modify the input feature set dynamically, thereby enhancing the adaptiveness of the model. In an online learning framework, the base models should also be time adaptive. To achieve this, a recursive algorithm is introduced, thus permitting the online updating of WGP base models. The proposed model has been tested on the actual data collected from both single and aggregated wind farms
A Method for Driving Route Predictions Based on Hidden Markov Model
Directory of Open Access Journals (Sweden)
Ning Ye
2015-01-01
Full Text Available We present a driving route prediction method that is based on Hidden Markov Model (HMM. This method can accurately predict a vehicle’s entire route as early in a trip’s lifetime as possible without inputting origins and destinations beforehand. Firstly, we propose the route recommendation system architecture, where route predictions play important role in the system. Secondly, we define a road network model, normalize each of driving routes in the rectangular coordinate system, and build the HMM to make preparation for route predictions using a method of training set extension based on K-means++ and the add-one (Laplace smoothing technique. Thirdly, we present the route prediction algorithm. Finally, the experimental results of the effectiveness of the route predictions that is based on HMM are shown.
On extended liability in a model of adverse selection
Dieter Balkenborg
2004-01-01
We consider a model where a judgment-proof firm needs finance to realize a project. This project might cause an environmental hazard with a probability that is the private knowledge of the firm. Thus there is asymmetric information with respect to the environmental riskiness of the project. We consider the implications of a simple joint and strict liability rule on the lender and the firm where, in case of a damage, the lender is responsible for that part of the liability which the judgment-p...
Xu, Zhiqiang
2017-02-16
Attributed graph clustering, also known as community detection on attributed graphs, attracts much interests recently due to the ubiquity of attributed graphs in real life. Many existing algorithms have been proposed for this problem, which are either distance based or model based. However, model selection in attributed graph clustering has not been well addressed, that is, most existing algorithms assume the cluster number to be known a priori. In this paper, we propose two efficient approaches for attributed graph clustering with automatic model selection. The first approach is a popular Bayesian nonparametric method, while the second approach is an asymptotic method based on a recently proposed model selection criterion, factorized information criterion. Experimental results on both synthetic and real datasets demonstrate that our approaches for attributed graph clustering with automatic model selection significantly outperform the state-of-the-art algorithm.
Bayesian Variable Selection in Multilevel Item Response Theory Models with Application in Genomics.
Fragoso, Tiago M; de Andrade, Mariza; Pereira, Alexandre C; Rosa, Guilherme J M; Soler, Júlia M P
2016-04-01
The goal of this paper is to present an implementation of stochastic search variable selection (SSVS) to multilevel model from item response theory (IRT). As experimental settings get more complex and models are required to integrate multiple (and sometimes massive) sources of information, a model that can jointly summarize and select the most relevant characteristics can provide better interpretation and a deeper insight into the problem. A multilevel IRT model recently proposed in the literature for modeling multifactorial diseases is extended to perform variable selection in the presence of thousands of covariates using SSVS. We derive conditional distributions required for such a task as well as an acceptance-rejection step that allows for the SSVS in high dimensional settings using a Markov Chain Monte Carlo algorithm. We validate the variable selection procedure through simulation studies, and illustrate its application on a study with genetic markers associated with the metabolic syndrome. © 2016 WILEY PERIODICALS, INC.
SU-F-R-10: Selecting the Optimal Solution for Multi-Objective Radiomics Model
International Nuclear Information System (INIS)
Zhou, Z; Folkert, M; Wang, J
2016-01-01
Purpose: To develop an evidential reasoning approach for selecting the optimal solution from a Pareto solution set obtained by a multi-objective radiomics model for predicting distant failure in lung SBRT. Methods: In the multi-objective radiomics model, both sensitivity and specificity are considered as the objective functions simultaneously. A Pareto solution set with many feasible solutions will be resulted from the multi-objective optimization. In this work, an optimal solution Selection methodology for Multi-Objective radiomics Learning model using the Evidential Reasoning approach (SMOLER) was proposed to select the optimal solution from the Pareto solution set. The proposed SMOLER method used the evidential reasoning approach to calculate the utility of each solution based on pre-set optimal solution selection rules. The solution with the highest utility was chosen as the optimal solution. In SMOLER, an optimal learning model coupled with clonal selection algorithm was used to optimize model parameters. In this study, PET, CT image features and clinical parameters were utilized for predicting distant failure in lung SBRT. Results: Total 126 solution sets were generated by adjusting predictive model parameters. Each Pareto set contains 100 feasible solutions. The solution selected by SMOLER within each Pareto set was compared to the manually selected optimal solution. Five-cross-validation was used to evaluate the optimal solution selection accuracy of SMOLER. The selection accuracies for five folds were 80.00%, 69.23%, 84.00%, 84.00%, 80.00%, respectively. Conclusion: An optimal solution selection methodology for multi-objective radiomics learning model using the evidential reasoning approach (SMOLER) was proposed. Experimental results show that the optimal solution can be found in approximately 80% cases.
Selective Cooperation in Early Childhood - How to Choose Models and Partners.
Directory of Open Access Journals (Sweden)
Jonas Hermes
Full Text Available Cooperation is essential for human society, and children engage in cooperation from early on. It is unclear, however, how children select their partners for cooperation. We know that children choose selectively whom to learn from (e.g. preferring reliable over unreliable models on a rational basis. The present study investigated whether children (and adults also choose their cooperative partners selectively and what model characteristics they regard as important for cooperative partners and for informants about novel words. Three- and four-year-old children (N = 64 and adults (N = 14 saw contrasting pairs of models differing either in physical strength or in accuracy (in labeling known objects. Participants then performed different tasks (cooperative problem solving and word learning requiring the choice of a partner or informant. Both children and adults chose their cooperative partners selectively. Moreover they showed the same pattern of selective model choice, regarding a wide range of model characteristics as important for cooperation (preferring both the strong and the accurate model for a strength-requiring cooperation tasks, but only prior knowledge as important for word learning (preferring the knowledgeable but not the strong model for word learning tasks. Young children's selective model choice thus reveals an early rational competence: They infer characteristics from past behavior and flexibly consider what characteristics are relevant for certain tasks.
Schmidtmann, I; Elsäßer, A; Weinmann, A; Binder, H
2014-12-30
For determining a manageable set of covariates potentially influential with respect to a time-to-event endpoint, Cox proportional hazards models can be combined with variable selection techniques, such as stepwise forward selection or backward elimination based on p-values, or regularized regression techniques such as component-wise boosting. Cox regression models have also been adapted for dealing with more complex event patterns, for example, for competing risks settings with separate, cause-specific hazard models for each event type, or for determining the prognostic effect pattern of a variable over different landmark times, with one conditional survival model for each landmark. Motivated by a clinical cancer registry application, where complex event patterns have to be dealt with and variable selection is needed at the same time, we propose a general approach for linking variable selection between several Cox models. Specifically, we combine score statistics for each covariate across models by Fisher's method as a basis for variable selection. This principle is implemented for a stepwise forward selection approach as well as for a regularized regression technique. In an application to data from hepatocellular carcinoma patients, the coupled stepwise approach is seen to facilitate joint interpretation of the different cause-specific Cox models. In conditional survival models at landmark times, which address updates of prediction as time progresses and both treatment and other potential explanatory variables may change, the coupled regularized regression approach identifies potentially important, stably selected covariates together with their effect time pattern, despite having only a small number of events. These results highlight the promise of the proposed approach for coupling variable selection between Cox models, which is particularly relevant for modeling for clinical cancer registries with their complex event patterns. Copyright © 2014 John Wiley & Sons
Fox, Eric W; Hill, Ryan A; Leibowitz, Scott G; Olsen, Anthony R; Thornbrugh, Darren J; Weber, Marc H
2017-07-01
Random forest (RF) modeling has emerged as an important statistical learning method in ecology due to its exceptional predictive performance. However, for large and complex ecological data sets, there is limited guidance on variable selection methods for RF modeling. Typically, either a preselected set of predictor variables are used or stepwise procedures are employed which iteratively remove variables according to their importance measures. This paper investigates the application of variable selection methods to RF models for predicting probable biological stream condition. Our motivating data set consists of the good/poor condition of n = 1365 stream survey sites from the 2008/2009 National Rivers and Stream Assessment, and a large set (p = 212) of landscape features from the StreamCat data set as potential predictors. We compare two types of RF models: a full variable set model with all 212 predictors and a reduced variable set model selected using a backward elimination approach. We assess model accuracy using RF's internal out-of-bag estimate, and a cross-validation procedure with validation folds external to the variable selection process. We also assess the stability of the spatial predictions generated by the RF models to changes in the number of predictors and argue that model selection needs to consider both accuracy and stability. The results suggest that RF modeling is robust to the inclusion of many variables of moderate to low importance. We found no substantial improvement in cross-validated accuracy as a result of variable reduction. Moreover, the backward elimination procedure tended to select too few variables and exhibited numerous issues such as upwardly biased out-of-bag accuracy estimates and instabilities in the spatial predictions. We use simulations to further support and generalize results from the analysis of real data. A main purpose of this work is to elucidate issues of model selection bias and instability to ecologists interested in
Impacts of selected dietary polyphenols on caramelization in model systems.
Zhang, Xinchen; Chen, Feng; Wang, Mingfu
2013-12-15
This study investigated the impacts of six dietary polyphenols (phloretin, naringenin, quercetin, epicatechin, chlorogenic acid and rosmarinic acid) on fructose caramelization in thermal model systems at either neutral or alkaline pH. These polyphenols were found to increase the browning intensity and antioxidant capacity of caramel. The chemical reactions in the system of sugar and polyphenol, which include formation of polyphenol-sugar adducts, were found to be partially responsible for the formation of brown pigments and heat-induced antioxidants based on instrumental analysis. In addition, rosmarinic acid was demonstrated to significantly inhibit the formation of 5-hydroxymethylfurfural (HMF). Thus this research added to the efforts of controlling caramelization by dietary polyphenols under thermal condition, and provided some evidence to propose dietary polyphenols as functional ingredients to modify the caramel colour and bioactivity as well as to lower the amount of heat-induced contaminants such as 5-hydroxymethylfurfural (HMF). Copyright © 2013 Elsevier Ltd. All rights reserved.
Selected topics in phenomenology of the standard model
International Nuclear Information System (INIS)
Roberts, R.G.
1991-01-01
These lectures cover some aspects of phenomenology of topics in high energy physics which advertise the success of the standard model in dealing with a wide variety of experimental data. First we begin with a look at deep inelastic scattering. This tells us about the structure of the nucleon, which is understood in terms of the SU(3) gauge theory of QCD, which then allows the information on quark and gluon distributions to be carried over to other 'hard' processes such as hadronic production of jets. Recent data on electroweak processes can estimate the value of Sin 2 θw to a precision where the inclusion of radiative corrections allow bounds to be made on the mass of the top quark. Electroweak effects arise in e + e - collisions, but we first present a review of the recent history of this topic within the context of QCD. We bring the subject up to date with a look at the physics at (or near) the Z pole where the measurement of asymmetries can give more information. We look at the conventional description of quark mixing by the CKM matrix and see how the mixing parameters are systematically being extracted from a variety of reactions and decays. In turn, the values can be used to set bounds on the top quark mass. The matter of CP violation in weak interactions is addressed within the context of the standard model, recent data on ε'/ε being the source of current excitement. Finally, we at the theoretical description and experimental efforts to search for the top quark. (author)
gamboostLSS: An R Package for Model Building and Variable Selection in the GAMLSS Framework
Hofner, Benjamin; Mayr, Andreas; Schmid, Matthias
2014-01-01
Generalized additive models for location, scale and shape are a flexible class of regression models that allow to model multiple parameters of a distribution function, such as the mean and the standard deviation, simultaneously. With the R package gamboostLSS, we provide a boosting method to fit these models. Variable selection and model choice are naturally available within this regularized regression framework. To introduce and illustrate the R package gamboostLSS and its infrastructure, we...
N. Sczygiol; R. Dyja
2007-01-01
Presented paper contains evaluation of influence of selected parameters on sensitivity of a numerical model of solidification. The investigated model is based on the heat conduction equation with a heat source and solved using the finite element method (FEM). The model is built with the use of enthalpy formulation for solidification and using an intermediate solid fraction growth model. The model sensitivity is studied with the use of Morris method, which is one of global sensitivity methods....
Graphical models for inferring single molecule dynamics
Directory of Open Access Journals (Sweden)
Gonzalez Ruben L
2010-10-01
Full Text Available Abstract Background The recent explosion of experimental techniques in single molecule biophysics has generated a variety of novel time series data requiring equally novel computational tools for analysis and inference. This article describes in general terms how graphical modeling may be used to learn from biophysical time series data using the variational Bayesian expectation maximization algorithm (VBEM. The discussion is illustrated by the example of single-molecule fluorescence resonance energy transfer (smFRET versus time data, where the smFRET time series is modeled as a hidden Markov model (HMM with Gaussian observables. A detailed description of smFRET is provided as well. Results The VBEM algorithm returns the model’s evidence and an approximating posterior parameter distribution given the data. The former provides a metric for model selection via maximum evidence (ME, and the latter a description of the model’s parameters learned from the data. ME/VBEM provide several advantages over the more commonly used approach of maximum likelihood (ML optimized by the expectation maximization (EM algorithm, the most important being a natural form of model selection and a well-posed (non-divergent optimization problem. Conclusions The results demonstrate the utility of graphical modeling for inference of dynamic processes in single molecule biophysics.
Model selection in Bayesian segmentation of multiple DNA alignments.
Oldmeadow, Christopher; Keith, Jonathan M
2011-03-01
The analysis of multiple sequence alignments is allowing researchers to glean valuable insights into evolution, as well as identify genomic regions that may be functional, or discover novel classes of functional elements. Understanding the distribution of conservation levels that constitutes the evolutionary landscape is crucial to distinguishing functional regions from non-functional. Recent evidence suggests that a binary classification of evolutionary rates is inappropriate for this purpose and finds only highly conserved functional elements. Given that the distribution of evolutionary rates is multi-modal, determining the number of modes is of paramount concern. Through simulation, we evaluate the performance of a number of information criterion approaches derived from MCMC simulations in determining the dimension of a model. We utilize a deviance information criterion (DIC) approximation that is more robust than the approximations from other information criteria, and show our information criteria approximations do not produce superfluous modes when estimating conservation distributions under a variety of circumstances. We analyse the distribution of conservation for a multiple alignment comprising four primate species and mouse, and repeat this on two additional multiple alignments of similar species. We find evidence of six distinct classes of evolutionary rates that appear to be robust to the species used. Source code and data are available at http://dl.dropbox.com/u/477240/changept.zip.
Multi-scale habitat selection modeling: A review and outlook
Kevin McGarigal; Ho Yi Wan; Kathy A. Zeller; Brad C. Timm; Samuel A. Cushman
2016-01-01
Scale is the lens that focuses ecological relationships. Organisms select habitat at multiple hierarchical levels and at different spatial and/or temporal scales within each level. Failure to properly address scale dependence can result in incorrect inferences in multi-scale habitat selection modeling studies.
Augmented Self-Modeling as a Treatment for Children with Selective Mutism.
Kehle, Thomas J.; Madaus, Melissa R.; Baratta, Victoria S.; Bray, Melissa A.
1998-01-01
Describes the treatment of three children experiencing selective mutism. The procedure utilized incorporated self-modeling, mystery motivators, self-reinforcement, stimulus fading, spacing, and antidepressant medication. All three children evidenced a complete cessation of selective mutism and maintained their treatment gains at follow-up.…
Towards a pro-health food-selection model for gatekeepers in ...
African Journals Online (AJOL)
The purpose of this study was to develop a pro-health food selection model for gatekeepers of Bulawayo high-density suburbs in Zimbabwe. Gatekeepers in five suburbs constituted the study population from which a sample of 250 subjects was randomly selected. Of the total respondents (N= 182), 167 had their own ...
Zarindast, Atousa; Seyed Hosseini, Seyed Mohamad; Pishvaee, Mir Saman
2017-11-01
Robust supplier selection problem, in a scenario-based approach has been proposed, when the demand and exchange rates are subject to uncertainties. First, a deterministic multi-objective mixed integer linear programming is developed; then, the robust counterpart of the proposed mixed integer linear programming is presented using the recent extension in robust optimization theory. We discuss decision variables, respectively, by a two-stage stochastic planning model, a robust stochastic optimization planning model which integrates worst case scenario in modeling approach and finally by equivalent deterministic planning model. The experimental study is carried out to compare the performances of the three models. Robust model resulted in remarkable cost saving and it illustrated that to cope with such uncertainties, we should consider them in advance in our planning. In our case study different supplier were selected due to this uncertainties and since supplier selection is a strategic decision, it is crucial to consider these uncertainties in planning approach.
Fuzzy decision-making: a new method in model selection via various validity criteria
International Nuclear Information System (INIS)
Shakouri Ganjavi, H.; Nikravesh, K.
2001-01-01
Modeling is considered as the first step in scientific investigations. Several alternative models may be candida ted to express a phenomenon. Scientists use various criteria to select one model between the competing models. Based on the solution of a Fuzzy Decision-Making problem, this paper proposes a new method in model selection. The method enables the scientist to apply all desired validity criteria, systematically by defining a proper Possibility Distribution Function due to each criterion. Finally, minimization of a utility function composed of the Possibility Distribution Functions will determine the best selection. The method is illustrated through a modeling example for the A verage Daily Time Duration of Electrical Energy Consumption in Iran
Towards a pro-health food-selection model for gatekeepers in ...
African Journals Online (AJOL)
. MINITAB Release 10.2-computer package was used to extract dimensions underlying gatekeepers' food selection. Two models, both of which were not health oriented, were determined from the study findings. Family networks received the ...
Frisch, Simon; Dshemuchadse, Maja; Görner, Max; Goschke, Thomas; Scherbaum, Stefan
2015-11-01
Selective attention biases information processing toward stimuli that are relevant for achieving our goals. However, the nature of this bias is under debate: Does it solely rely on the amplification of goal-relevant information or is there a need for additional inhibitory processes that selectively suppress currently distracting information? Here, we explored the processes underlying selective attention with a dynamic, modeling-based approach that focuses on the continuous evolution of behavior over time. We present two dynamic neural field models incorporating the diverging theoretical assumptions. Simulations with both models showed that they make similar predictions with regard to response times but differ markedly with regard to their continuous behavior. Human data observed via mouse tracking as a continuous measure of performance revealed evidence for the model solely based on amplification but no indication of persisting selective distracter inhibition.
Decision support model for selecting and evaluating suppliers in the construction industry
Directory of Open Access Journals (Sweden)
Fernando Schramm
2012-12-01
Full Text Available A structured evaluation of the construction industry's suppliers, considering aspects which make their quality and credibility evident, can be a strategic tool to manage this specific supply chain. This study proposes a multi-criteria decision model for suppliers' selection from the construction industry, as well as an efficient evaluation procedure for the selected suppliers. The model is based on SMARTER (Simple Multi-Attribute Rating Technique Exploiting Ranking method and its main contribution is a new approach to structure the process of suppliers' selection, establishing explicit strategic policies on which the company management system relied to make the suppliers selection. This model was applied to a Civil Construction Company in Brazil and the main results demonstrate the efficiency of the proposed model. This study allowed the development of an approach to Construction Industry which was able to provide a better relationship among its managers, suppliers and partners.
Traditional and robust vector selection methods for use with similarity based models
International Nuclear Information System (INIS)
Hines, J. W.; Garvey, D. R.
2006-01-01
Vector selection, or instance selection as it is often called in the data mining literature, performs a critical task in the development of nonparametric, similarity based models. Nonparametric, similarity based modeling (SBM) is a form of 'lazy learning' which constructs a local model 'on the fly' by comparing a query vector to historical, training vectors. For large training sets the creation of local models may become cumbersome, since each training vector must be compared to the query vector. To alleviate this computational burden, varying forms of training vector sampling may be employed with the goal of selecting a subset of the training data such that the samples are representative of the underlying process. This paper describes one such SBM, namely auto-associative kernel regression (AAKR), and presents five traditional vector selection methods and one robust vector selection method that may be used to select prototype vectors from a larger data set in model training. The five traditional vector selection methods considered are min-max, vector ordering, combination min-max and vector ordering, fuzzy c-means clustering, and Adeli-Hung clustering. Each method is described in detail and compared using artificially generated data and data collected from the steam system of an operating nuclear power plant. (authors)
Model selection for dynamical systems via sparse regression and information criteria.
Mangan, N M; Kutz, J N; Brunton, S L; Proctor, J L
2017-08-01
We develop an algorithm for model selection which allows for the consideration of a combinatorially large number of candidate models governing a dynamical system. The innovation circumvents a disadvantage of standard model selection which typically limits the number of candidate models considered due to the intractability of computing information criteria. Using a recently developed sparse identification of nonlinear dynamics algorithm, the sub-selection of candidate models near the Pareto frontier allows feasible computation of Akaike information criteria (AIC) or Bayes information criteria scores for the remaining candidate models. The information criteria hierarchically ranks the most informative models, enabling the automatic and principled selection of the model with the strongest support in relation to the time-series data. Specifically, we show that AIC scores place each candidate model in the strong support , weak support or no support category. The method correctly recovers several canonical dynamical systems, including a susceptible-exposed-infectious-recovered disease model, Burgers' equation and the Lorenz equations, identifying the correct dynamical system as the only candidate model with strong support.
On market timing and portfolio selectivity: modifying the Henriksson-Merton model
Goś, Krzysztof
2011-01-01
This paper evaluates selected functionalities of the parametrical Henriksson-Merton test, a tool designed for measuring the market timing and portfolio selectivity capabilities. It also provides a solution to two significant disadvantages of the model: relatively indirect interpretation and vulnerability to parameter insignificance. The model has been put to test on a group of Polish mutual funds in a period of 63 months (January 2004 – March 2009), providing unsatisfa...
Stock Selection for Portfolios Using Expected Utility-Entropy Decision Model
Directory of Open Access Journals (Sweden)
Jiping Yang
2017-09-01
Full Text Available Yang and Qiu proposed and then recently improved an expected utility-entropy (EU-E measure of risk and decision model. When segregation holds, Luce et al. derived an expected utility term, plus a constant multiplies the Shannon entropy as the representation of risky choices, further demonstrating the reasonability of the EU-E decision model. In this paper, we apply the EU-E decision model to selecting the set of stocks to be included in the portfolios. We first select 7 and 10 stocks from the 30 component stocks of Dow Jones Industrial Average index, and then derive and compare the efficient portfolios in the mean-variance framework. The conclusions imply that efficient portfolios composed of 7(10 stocks selected using the EU-E model with intermediate intervals of the tradeoff coefficients are more efficient than that composed of the sets of stocks selected using the expected utility model. Furthermore, the efficient portfolio of 7(10 stocks selected by the EU-E decision model have almost the same efficient frontier as that of the sample of all stocks. This suggests the necessity of incorporating both the expected utility and Shannon entropy together when taking risky decisions, further demonstrating the importance of Shannon entropy as the measure of uncertainty, as well as the applicability of the EU-E model as a decision-making model.
Hidden Markov Models in Bioinformatics: SNV Inference from Next Generation Sequence.
Bian, Jiawen; Zhou, Xiaobo
2017-01-01
The rapid development of next generation sequencing (NGS) technology provides a novel avenue for genomic exploration and research. Hidden Markov models (HMMs) have wide applications in pattern recognition as well as Bioinformatics such as transcription factor binding sites and cis-regulatory modules detection. An application of HMM is introduced in this chapter with the in-deep developing of NGS. Single nucleotide variants (SNVs) inferred from NGS are expected to reveal gene mutations in cancer. However, NGS has lower sequence coverage and poor SNV detection capability in the regulatory regions of the genome. A specific HMM is developed for this purpose to infer the genotype for each position on the genome by incorporating the mapping quality of each read and the corresponding base quality on the reads into the emission probability of HMM. The procedure and the implementation of the algorithm is presented in detail for understanding and programming.
Differences between selection on sex versus recombination in red queen models with diploid hosts.
Agrawal, Aneil F
2009-08-01
The Red Queen hypothesis argues that parasites generate selection for genetic mixing (sex and recombination) in their hosts. A number of recent papers have examined this hypothesis using models with haploid hosts. In these haploid models, sex and recombination are selectively equivalent. However, sex and recombination are not equivalent in diploids because selection on sex depends on the consequences of segregation as well as recombination. Here I compare how parasites select on modifiers of sexual reproduction and modifiers of recombination rate. Across a wide set of parameters, parasites tend to select against both sex and recombination, though recombination is favored more often than is sex. There is little correspondence between the conditions favoring sex and those favoring recombination, indicating that the direction of selection on sex is often determined by the effects of segregation, not recombination. Moreover, when sex was favored it is usually due to a long-term advantage whereas short-term effects are often responsible for selection favoring recombination. These results strongly indicate that Red Queen models focusing exclusively on the effects of recombination cannot be used to infer the type of selection on sex that is generated by parasites on diploid hosts.
Optimisation of Hidden Markov Model using Baum–Welch algorithm ...
Indian Academy of Sciences (India)
Home; Journals; Journal of Earth System Science; Volume 126; Issue 1. Optimisation of Hidden Markov ... The present work is a part of development of Hidden Markov Model (HMM) based avalanche forecasting system for Pir-Panjal and Great Himalayan mountain ranges of the Himalaya. In this work, HMMs have been ...
Bayesian Modelling of fMRI Time Series
DEFF Research Database (Denmark)
Højen-Sørensen, Pedro; Hansen, Lars Kai; Rasmussen, Carl Edward
2000-01-01
We present a Hidden Markov Model (HMM) for inferring the hidden psychological state (or neural activity) during single trial fMRI activation experiments with blocked task paradigms. Inference is based on Bayesian methodology, using a combination of analytical and a variety of Markov Chain Monte...
Evolving the Topology of Hidden Markov Models using Evolutionary Algorithms
DEFF Research Database (Denmark)
Thomsen, Réne
2002-01-01
Hidden Markov models (HMM) are widely used for speech recognition and have recently gained a lot of attention in the bioinformatics community, because of their ability to capture the information buried in biological sequences. Usually, heuristic algorithms such as Baum-Welch are used to estimate ...
Hidden Markov model-based approach for generation of Pitman ...
Indian Academy of Sciences (India)
In this paper, an approach for feature extraction using Mel frequency cep- stral coefficients (MFCC) and classification using hidden Markov models (HMM) for generating strokes comprising consonants and vowels (CV) in the process of production of Pitman shorthand language from spoken English is proposed. The.
International Nuclear Information System (INIS)
Elsheikh, Ahmed H.; Wheeler, Mary F.; Hoteit, Ibrahim
2014-01-01
A Hybrid Nested Sampling (HNS) algorithm is proposed for efficient Bayesian model calibration and prior model selection. The proposed algorithm combines, Nested Sampling (NS) algorithm, Hybrid Monte Carlo (HMC) sampling and gradient estimation using Stochastic Ensemble Method (SEM). NS is an efficient sampling algorithm that can be used for Bayesian calibration and estimating the Bayesian evidence for prior model selection. Nested sampling has the advantage of computational feasibility. Within the nested sampling algorithm, a constrained sampling step is performed. For this step, we utilize HMC to reduce the correlation between successive sampled states. HMC relies on the gradient of the logarithm of the posterior distribution, which we estimate using a stochastic ensemble method based on an ensemble of directional derivatives. SEM only requires forward model runs and the simulator is then used as a black box and no adjoint code is needed. The developed HNS algorithm is successfully applied for Bayesian calibration and prior model selection of several nonlinear subsurface flow problems
A concurrent optimization model for supplier selection with fuzzy quality loss
Energy Technology Data Exchange (ETDEWEB)
Rosyidi, C.; Murtisari, R.; Jauhari, W.
2017-07-01
The purpose of this research is to develop a concurrent supplier selection model to minimize the purchasing cost and fuzzy quality loss considering process capability and assembled product specification. Design/methodology/approach: This research integrates fuzzy quality loss in the model to concurrently solve the decision making in detailed design stage and manufacturing stage. Findings: The resulted model can be used to concurrently select the optimal supplier and determine the tolerance of the components. The model balances the purchasing cost and fuzzy quality loss. Originality/value: An assembled product consists of many components which must be purchased from the suppliers. Fuzzy quality loss is integrated in the supplier selection model to allow the vagueness in final assembly by grouping the assembly into several grades according to the resulted assembly tolerance.
Zhou, Ligang; Keung Lai, Kin; Yen, Jerome
2014-03-01
Due to the economic significance of bankruptcy prediction of companies for financial institutions, investors and governments, many quantitative methods have been used to develop effective prediction models. Support vector machine (SVM), a powerful classification method, has been used for this task; however, the performance of SVM is sensitive to model form, parameter setting and features selection. In this study, a new approach based on direct search and features ranking technology is proposed to optimise features selection and parameter setting for 1-norm and least-squares SVM models for bankruptcy prediction. This approach is also compared to the SVM models with parameter optimisation and features selection by the popular genetic algorithm technique. The experimental results on a data set with 2010 instances show that the proposed models are good alternatives for bankruptcy prediction.
A concurrent optimization model for supplier selection with fuzzy quality loss
International Nuclear Information System (INIS)
Rosyidi, C.; Murtisari, R.; Jauhari, W.
2017-01-01
The purpose of this research is to develop a concurrent supplier selection model to minimize the purchasing cost and fuzzy quality loss considering process capability and assembled product specification. Design/methodology/approach: This research integrates fuzzy quality loss in the model to concurrently solve the decision making in detailed design stage and manufacturing stage. Findings: The resulted model can be used to concurrently select the optimal supplier and determine the tolerance of the components. The model balances the purchasing cost and fuzzy quality loss. Originality/value: An assembled product consists of many components which must be purchased from the suppliers. Fuzzy quality loss is integrated in the supplier selection model to allow the vagueness in final assembly by grouping the assembly into several grades according to the resulted assembly tolerance.
Island-Model Genomic Selection for Long-Term Genetic Improvement of Autogamous Crops.
Yabe, Shiori; Yamasaki, Masanori; Ebana, Kaworu; Hayashi, Takeshi; Iwata, Hiroyoshi
2016-01-01
Acceleration of genetic improvement of autogamous crops such as wheat and rice is necessary to increase cereal production in response to the global food crisis. Population and pedigree methods of breeding, which are based on inbred line selection, are used commonly in the genetic improvement of autogamous crops. These methods, however, produce a few novel combinations of genes in a breeding population. Recurrent selection promotes recombination among genes and produces novel combinations of genes in a breeding population, but it requires inaccurate single-plant evaluation for selection. Genomic selection (GS), which can predict genetic potential of individuals based on their marker genotype, might have high reliability of single-plant evaluation and might be effective in recurrent selection. To evaluate the efficiency of recurrent selection with GS, we conducted simulations using real marker genotype data of rice cultivars. Additionally, we introduced the concept of an "island model" inspired by evolutionary algorithms that might be useful to maintain genetic variation through the breeding process. We conducted GS simulations using real marker genotype data of rice cultivars to evaluate the efficiency of recurrent selection and the island model in an autogamous species. Results demonstrated the importance of producing novel combinations of genes through recurrent selection. An initial population derived from admixture of multiple bi-parental crosses showed larger genetic gains than a population derived from a single bi-parental cross in whole cycles, suggesting the importance of genetic variation in an initial population. The island-model GS better maintained genetic improvement in later generations than the other GS methods, suggesting that the island-model GS can utilize genetic variation in breeding and can retain alleles with small effects in the breeding population. The island-model GS will become a new breeding method that enhances the potential of genomic
Island-Model Genomic Selection for Long-Term Genetic Improvement of Autogamous Crops.
Directory of Open Access Journals (Sweden)
Shiori Yabe
Full Text Available Acceleration of genetic improvement of autogamous crops such as wheat and rice is necessary to increase cereal production in response to the global food crisis. Population and pedigree methods of breeding, which are based on inbred line selection, are used commonly in the genetic improvement of autogamous crops. These methods, however, produce a few novel combinations of genes in a breeding population. Recurrent selection promotes recombination among genes and produces novel combinations of genes in a breeding population, but it requires inaccurate single-plant evaluation for selection. Genomic selection (GS, which can predict genetic potential of individuals based on their marker genotype, might have high reliability of single-plant evaluation and might be effective in recurrent selection. To evaluate the efficiency of recurrent selection with GS, we conducted simulations using real marker genotype data of rice cultivars. Additionally, we introduced the concept of an "island model" inspired by evolutionary algorithms that might be useful to maintain genetic variation through the breeding process. We conducted GS simulations using real marker genotype data of rice cultivars to evaluate the efficiency of recurrent selection and the island model in an autogamous species. Results demonstrated the importance of producing novel combinations of genes through recurrent selection. An initial population derived from admixture of multiple bi-parental crosses showed larger genetic gains than a population derived from a single bi-parental cross in whole cycles, suggesting the importance of genetic variation in an initial population. The island-model GS better maintained genetic improvement in later generations than the other GS methods, suggesting that the island-model GS can utilize genetic variation in breeding and can retain alleles with small effects in the breeding population. The island-model GS will become a new breeding method that enhances the
Yang, Ziheng; Zhu, Tianqi
2018-02-20
The Bayesian method is noted to produce spuriously high posterior probabilities for phylogenetic trees in analysis of large datasets, but the precise reasons for this overconfidence are unknown. In general, the performance of Bayesian selection of misspecified models is poorly understood, even though this is of great scientific interest since models are never true in real data analysis. Here we characterize the asymptotic behavior of Bayesian model selection and show that when the competing models are equally wrong, Bayesian model selection exhibits surprising and polarized behaviors in large datasets, supporting one model with full force while rejecting the others. If one model is slightly less wrong than the other, the less wrong model will eventually win when the amount of data increases, but the method may become overconfident before it becomes reliable. We suggest that this extreme behavior may be a major factor for the spuriously high posterior probabilities for evolutionary trees. The philosophical implications of our results to the application of Bayesian model selection to evaluate opposing scientific hypotheses are yet to be explored, as are the behaviors of non-Bayesian methods in similar situations.
Directory of Open Access Journals (Sweden)
Vaida Marius
2009-12-01
Full Text Available In realizing this study I started from the premise that, by elaborating certain orientation models and initial selection for the speed skating and their application will appear superior results, necessary results, taking into account the actual evolution of the high performance sport in general and of the speed skating, in special.The target of this study has been the identification of an orientation model and a complete initial selection that should be based on the favorable aptitudes of the speed skating. On the basis of the made researched orientation models and initial selection has been made, things that have been demonstrated experimental that are not viable, the study starting from the data of the 120 copies, the complete experiment being made by 32 subjects separated in two groups, one using the proposed model and the other formed fromsubjects randomly selected.These models can serve as common working instruments both for the orientation process and for the initial selection one, being able to integrate in the proper practical activity, these being used easily both by coaches that are in charge with the proper selection of the athletes but also by the physical education teachers orschool teachers that are in contact with children of an early age.
Shi, Jinfei; Zhu, Songqing; Chen, Ruwen
2017-12-01
An order selection method based on multiple stepwise regressions is proposed for General Expression of Nonlinear Autoregressive model which converts the model order problem into the variable selection of multiple linear regression equation. The partial autocorrelation function is adopted to define the linear term in GNAR model. The result is set as the initial model, and then the nonlinear terms are introduced gradually. Statistics are chosen to study the improvements of both the new introduced and originally existed variables for the model characteristics, which are adopted to determine the model variables to retain or eliminate. So the optimal model is obtained through data fitting effect measurement or significance test. The simulation and classic time-series data experiment results show that the method proposed is simple, reliable and can be applied to practical engineering.
Rustamov, Samir; Mustafayev, Elshan; Clements, Mark A.
2018-04-01
The context analysis of customer requests in a natural language call routing problem is investigated in the paper. One of the most significant problems in natural language call routing is a comprehension of client request. With the aim of finding a solution to this issue, the Hybrid HMM and ANFIS models become a subject to an examination. Combining different types of models (ANFIS and HMM) can prevent misunderstanding by the system for identification of user intention in dialogue system. Based on these models, the hybrid system may be employed in various language and call routing domains due to nonusage of lexical or syntactic analysis in classification process.
Modeling of Clostridium tyrobutyricum for Butyric Acid Selectivity in Continuous Fermentation
Directory of Open Access Journals (Sweden)
Jianjun Du
2014-04-01
Full Text Available A mathematical model was developed to describe batch and continuous fermentation of glucose to organic acids with Clostridium tyrobutyricum. A modified Monod equation was used to describe cell growth, and a Luedeking-Piret equation was used to describe the production of butyric and acetic acids. Using the batch fermentation equations, models predicting butyric acid selectivity for continuous fermentation were also developed. The model showed that butyric acid production was a strong function of cell mass, while acetic acid production was a function of cell growth rate. Further, it was found that at high acetic acid concentrations, acetic acid was metabolized to butyric acid and that this conversion could be modeled. In batch fermentation, high butyric acid selectivity occurred at high initial cell or glucose concentrations. In continuous fermentation, decreased dilution rate improved selectivity; at a dilution rate of 0.028 h−1, the selectivity reached 95.8%. The model and experimental data showed that at total cell recycle, the butyric acid selectivity could reach 97.3%. This model could be used to optimize butyric acid production using C. tyrobutyricum in a continuous fermentation scheme. This is the first study that mathematically describes batch, steady state, and dynamic behavior of C. tyrobutyricum for butyric acid production.
Directory of Open Access Journals (Sweden)
Xiaofeng Lv
2018-01-01
Full Text Available Sensor data-based test selection optimization is the basis for designing a test work, which ensures that the system is tested under the constraint of the conventional indexes such as fault detection rate (FDR and fault isolation rate (FIR. From the perspective of equipment maintenance support, the ambiguity isolation has a significant effect on the result of test selection. In this paper, an improved test selection optimization model is proposed by considering the ambiguity degree of fault isolation. In the new model, the fault test dependency matrix is adopted to model the correlation between the system fault and the test group. The objective function of the proposed model is minimizing the test cost with the constraint of FDR and FIR. The improved chaotic discrete particle swarm optimization (PSO algorithm is adopted to solve the improved test selection optimization model. The new test selection optimization model is more consistent with real complicated engineering systems. The experimental result verifies the effectiveness of the proposed method.
Use of the AIC with the EM algorithm: A demonstration of a probability model selection technique
Energy Technology Data Exchange (ETDEWEB)
Glosup, J.G.; Axelrod M.C. [Lawrence Livermore National Lab., CA (United States)
1994-11-15
The problem of discriminating between two potential probability models, a Gaussian distribution and a mixture of Gaussian distributions, is considered. The focus of our interest is a case where the models are potentially non-nested and the parameters of the mixture model are estimated through the EM algorithm. The AIC, which is frequently used as a criterion for discriminating between non-nested models, is modified to work with the EM algorithm and is shown to provide a model selection tool for this situation. A particular problem involving an infinite mixture distribution known as Middleton`s Class A model is used to demonstrate the effectiveness and limitations of this method.
Optimal Selection of the Sampling Interval for Estimation of Modal Parameters by an ARMA- Model
DEFF Research Database (Denmark)
Kirkegaard, Poul Henning
1993-01-01
Optimal selection of the sampling interval for estimation of the modal parameters by an ARMA-model for a white noise loaded structure modelled as a single degree of- freedom linear mechanical system is considered. An analytical solution for an optimal uniform sampling interval, which is optimal...
Selecting ELL Textbooks: A Content Analysis of Language-Teaching Models
LaBelle, Jeffrey T.
2011-01-01
Many middle school teachers lack adequate criteria to critically select materials that represent a variety of L2 teaching models. This study analyzes the illustrated and written content of 33 ELL textbooks to determine the range of L2 teaching models represented. The researchers asked to what extent do middle school ELL texts depict frequency and…
DEFF Research Database (Denmark)
Ruano, MV; Ribes, J; de Pauw, DJW
2007-01-01
In this work we address the issue of parameter subset selection within the scope of activated sludge model calibration. To this end, we evaluate two approaches: (i) systems analysis and (ii) experience-based approach. The evaluation has been carried out using a dynamic model (ASM2d) calibrated...
Discounting model selection with area-based measures: A case for numerical integration.
Gilroy, Shawn P; Hantula, Donald A
2018-03-01
A novel method for analyzing delay discounting data is proposed. This newer metric, a model-based Area Under Curve (AUC) combining approximate Bayesian model selection and numerical integration, was compared to the point-based AUC methods developed by Myerson, Green, and Warusawitharana (2001) and extended by Borges, Kuang, Milhorn, and Yi (2016). Using data from computer simulation and a published study, comparisons of these methods indicated that a model-based form of AUC offered a more consistent and statistically robust measurement of area than provided by using point-based methods alone. Beyond providing a form of AUC directly from a discounting model, numerical integration methods permitted a general calculation in cases when the Effective Delay 50 (ED50) measure could not be calculated. This allowed discounting model selection to proceed in conditions where data are traditionally more challenging to model and measure, a situation where point-based AUC methods are often enlisted. Results from simulation and existing data indicated that numerical integration methods extended both the area-based interpretation of delay discounting as well as the discounting model selection approach. Limitations of point-based AUC as a first-line analysis of discounting and additional extensions of discounting model selection were also discussed. © 2018 Society for the Experimental Analysis of Behavior.
Testing the normality assumption in the sample selection model with an application to travel demand
van der Klaauw, B.; Koning, R.H.
2003-01-01
In this article we introduce a test for the normality assumption in the sample selection model. The test is based on a flexible parametric specification of the density function of the error terms in the model. This specification follows a Hermite series with bivariate normality as a special case.
Testing the normality assumption in the sample selection model with an application to travel demand
van der Klauw, B.; Koning, R.H.
In this article we introduce a test for the normality assumption in the sample selection model. The test is based on a flexible parametric specification of the density function of the error terms in the model. This specification follows a Hermite series with bivariate normality as a special case.
Raaijmakers, Steven F.; Baars, Martine; Schaap, Lydia; Paas, Fred; van Merriënboer, Jeroen; van Gog, Tamara
2018-01-01
Self-assessment and task-selection skills are crucial in self-regulated learning situations in which students can choose their own tasks. Prior research suggested that training with video modeling examples, in which another person (the model) demonstrates and explains the cyclical process of problem-solving task performance, self-assessment, and…
A Framework for Bioacoustic Vocalization Analysis Using Hidden Markov Models
Directory of Open Access Journals (Sweden)
Ebenezer Out-Nyarko
2009-11-01
Full Text Available Using Hidden Markov Models (HMMs as a recognition framework for automatic classification of animal vocalizations has a number of benefits, including the ability to handle duration variability through nonlinear time alignment, the ability to incorporate complex language or recognition constraints, and easy extendibility to continuous recognition and detection domains. In this work, we apply HMMs to several different species and bioacoustic tasks using generalized spectral features that can be easily adjusted across species and HMM network topologies suited to each task. This experimental work includes a simple call type classification task using one HMM per vocalization for repertoire analysis of Asian elephants, a language-constrained song recognition task using syllable models as base units for ortolan bunting vocalizations, and a stress stimulus differentiation task in poultry vocalizations using a non-sequential model via a one-state HMM with Gaussian mixtures. Results show strong performance across all tasks and illustrate the flexibility of the HMM framework for a variety of species, vocalization types, and analysis tasks.
Labonne, Jacques; Hendry, Andrew P
2010-07-01
The standard predictions of ecological speciation might be nuanced by the interaction between natural and sexual selection. We investigated this hypothesis with an individual-based model tailored to the biology of guppies (Poecilia reticulata). We specifically modeled the situation where a high-predation population below a waterfall colonizes a low-predation population above a waterfall. Focusing on the evolution of male color, we confirm that divergent selection causes the appreciable evolution of male color within 20 generations. The rate and magnitude of this divergence were reduced when dispersal rates were high and when female choice did not differ between environments. Adaptive divergence was always coupled to the evolution of two reproductive barriers: viability selection against immigrants and hybrids. Different types of sexual selection, however, led to contrasting results for another potential reproductive barrier: mating success of immigrants. In some cases, the effects of natural and sexual selection offset each other, leading to no overall reproductive isolation despite strong adaptive divergence. Sexual selection acting through female choice can thus strongly modify the effects of divergent natural selection and thereby alter the standard predictions of ecological speciation. We also found that under no circumstances did divergent selection cause appreciable divergence in neutral genetic markers.
Preprocessing for Optimization of Probabilistic-Logic Models for Sequence Analysis
DEFF Research Database (Denmark)
Christiansen, Henning; Lassen, Ole Torp
2009-01-01
A class of probabilistic-logic models is considered, which increases the expressibility from HMM's and SCFG's regular and context-free languages to, in principle, Turing complete languages. In general, such models are computationally far too complex for direct use, so optimization by pruning...
A finite volume alternate direction implicit approach to modeling selective laser melting
DEFF Research Database (Denmark)
Hattel, Jesper Henri; Mohanty, Sankhya
2013-01-01
Over the last decade, several studies have attempted to develop thermal models for analyzing the selective laser melting process with a vision to predict thermal stresses, microstructures and resulting mechanical properties of manufactured products. While a holistic model addressing all involved...... is proposed for modeling single-layer and few-layers selective laser melting processes. The ADI technique is implemented and applied for two cases involving constant material properties and non-linear material behavior. The ADI FV method consume less time while having comparable accuracy with respect to 3D...
SOME USES OF MODELS OF QUANTITATIVE GENETIC SELECTION IN SOCIAL SCIENCE.
Weight, Michael D; Harpending, Henry
2017-01-01
The theory of selection of quantitative traits is widely used in evolutionary biology, agriculture and other related fields. The fundamental model known as the breeder's equation is simple, robust over short time scales, and it is often possible to estimate plausible parameters. In this paper it is suggested that the results of this model provide useful yardsticks for the description of social traits and the evaluation of transmission models. The differences on a standard personality test between samples of Old Order Amish and Indiana rural young men from the same county and the decline of homicide in Medieval Europe are used as illustrative examples of the overall approach. It is shown that the decline of homicide is unremarkable under a threshold model while the differences between rural Amish and non-Amish young men are too large to be a plausible outcome of simple genetic selection in which assortative mating by affiliation is equivalent to truncation selection.
Mutation-selection dynamics and error threshold in an evolutionary model for Turing machines.
Musso, Fabio; Feverati, Giovanni
2012-01-01
We investigate the mutation-selection dynamics for an evolutionary computation model based on Turing machines. The use of Turing machines allows for very simple mechanisms of code growth and code activation/inactivation through point mutations. To any value of the point mutation probability corresponds a maximum amount of active code that can be maintained by selection and the Turing machines that reach it are said to be at the error threshold. Simulations with our model show that the Turing machines population evolve toward the error threshold. Mathematical descriptions of the model point out that this behaviour is due more to the mutation-selection dynamics than to the intrinsic nature of the Turing machines. This indicates that this result is much more general than the model considered here and could play a role also in biological evolution. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.
A Multi-objective model for selection of projects to finance new enterprise SMEs in Colombia
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J.R. Coronado-Hernández
2011-10-01
Full Text Available Purpose: This paper presents a multi-objective programming model for selection of Projects for Financing New Enterprise SMEs in Colombia with objectivity and transparency in every call. Approach: The model has four social objectives, subject to constraint budget and to the requirements of every summons. The resolution procedure for the model is based on principles of goal programming. Findings: Selection projects subject to the impact within the country. Research limitations: The selection of the projects is restricted by a legal framework, the terms of reference and the budget of the summons. Practical implications: The projects must be viable according to the characteristics of every summons. Originality/value: The suggested model offers an alternative for entities that need to evaluate projects of co-financing for the managerial development of the SMEs with more objectivity and transparency in the assignment of resources.
Supplier selection based on a neural network model using genetic algorithm.
Golmohammadi, Davood; Creese, Robert C; Valian, Haleh; Kolassa, John
2009-09-01
In this paper, a decision-making model was developed to select suppliers using neural networks (NNs). This model used historical supplier performance data for selection of vendor suppliers. Input and output were designed in a unique manner for training purposes. The managers' judgments about suppliers were simulated by using a pairwise comparisons matrix for output estimation in the NN. To obtain the benefit of a search technique for model structure and training, genetic algorithm (GA) was applied for the initial weights and architecture of the network. The suppliers' database information (input) can be updated over time to change the suppliers' score estimation based on their performance. The case study illustrated shows how the model can be applied for suppliers' selection.
Orbital-selective Mott phase in multiorbital models for iron pnictides and chalcogenides
Yu, Rong; Si, Qimiao
2017-09-01
There is increasing recognition that the multiorbital nature of the 3 d electrons is important to the proper description of the electronic states in the normal state of the iron-based superconductors. Earlier studies of the pertinent multiorbital Hubbard models identified an orbital-selective Mott phase, which anchors the orbital-selective behavior seen in the overall phase diagram. An important characteristics of the models is that the orbitals are kinetically coupled, i.e., hybridized, to each other, which makes the orbital-selective Mott phase especially nontrivial. A U (1 ) slave-spin method was used to analyze the model with nonzero orbital-level splittings. Here we develop a Landau free-energy functional to shed further light on this issue. We put the microscopic analysis from the U (1 ) slave-spin approach in this perspective, and show that the intersite spin correlations are crucial to the renormalization of the bare hybridization amplitude towards zero and the concomitant realization of the orbital-selective Mott transition. Based on this insight, we discuss additional ways to study the orbital-selective Mott physics from a dynamical competition between the interorbital hybridization and collective spin correlations. Our results demonstrate the robustness of the orbital-selective Mott phase in the multiorbital models appropriate for the iron-based superconductors.
He, Zhangyi; Beaumont, Mark; Yu, Feng
2017-07-05
We explore the effect of different mechanisms of natural selection on the evolution of populations for one- and two-locus systems. We compare the effect of viability and fecundity selection in the context of the Wright-Fisher model with selection under the assumption of multiplicative fitness. We show that these two modes of natural selection correspond to different orderings of the processes of population regulation and natural selection in the Wright-Fisher model. We find that under the Wright-Fisher model these two different orderings can affect the distribution of trajectories of haplotype frequencies evolving with genetic recombination. However, the difference in the distribution of trajectories is only appreciable when the population is in significant linkage disequilibrium. We find that as linkage disequilibrium decays the trajectories for the two different models rapidly become indistinguishable. We discuss the significance of these findings in terms of biological examples of viability and fecundity selection, and speculate that the effect may be significant when factors such as gene migration maintain a degree of linkage disequilibrium. Copyright © 2017 He et al.
Lam, Lun Tak; Sun, Yi; Davey, Neil; Adams, Rod; Prapopoulou, Maria; Brown, Marc B; Moss, Gary P
2010-06-01
The aim was to employ Gaussian processes to assess mathematically the nature of a skin permeability dataset and to employ these methods, particularly feature selection, to determine the key physicochemical descriptors which exert the most significant influence on percutaneous absorption, and to compare such models with established existing models. Gaussian processes, including automatic relevance detection (GPRARD) methods, were employed to develop models of percutaneous absorption that identified key physicochemical descriptors of percutaneous absorption. Using MatLab software, the statistical performance of these models was compared with single linear networks (SLN) and quantitative structure-permeability relationships (QSPRs). Feature selection methods were used to examine in more detail the physicochemical parameters used in this study. A range of statistical measures to determine model quality were used. The inherently nonlinear nature of the skin data set was confirmed. The Gaussian process regression (GPR) methods yielded predictive models that offered statistically significant improvements over SLN and QSPR models with regard to predictivity (where the rank order was: GPR > SLN > QSPR). Feature selection analysis determined that the best GPR models were those that contained log P, melting point and the number of hydrogen bond donor groups as significant descriptors. Further statistical analysis also found that great synergy existed between certain parameters. It suggested that a number of the descriptors employed were effectively interchangeable, thus questioning the use of models where discrete variables are output, usually in the form of an equation. The use of a nonlinear GPR method produced models with significantly improved predictivity, compared with SLN or QSPR models. Feature selection methods were able to provide important mechanistic information. However, it was also shown that significant synergy existed between certain parameters, and as such it
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Sveiczer Akos
2006-03-01
Full Text Available Abstract Background There is considerable controversy concerning the exact growth profile of size parameters during the cell cycle. Linear, exponential and bilinear models are commonly considered, and the same model may not apply for all species. Selection of the most adequate model to describe a given data-set requires the use of quantitative model selection criteria, such as the partial (sequential F-test, the Akaike information criterion and the Schwarz Bayesian information criterion, which are suitable for comparing differently parameterized models in terms of the quality and robustness of the fit but have not yet been used in cell growth-profile studies. Results Length increase data from representative individual fission yeast (Schizosaccharomyces pombe cells measured on time-lapse films have been reanalyzed using these model selection criteria. To fit the data, an extended version of a recently introduced linearized biexponential (LinBiExp model was developed, which makes possible a smooth, continuously differentiable transition between two linear segments and, hence, allows fully parametrized bilinear fittings. Despite relatively small differences, essentially all the quantitative selection criteria considered here indicated that the bilinear model was somewhat more adequate than the exponential model for fitting these fission yeast data. Conclusion A general quantitative framework was introduced to judge the adequacy of bilinear versus exponential models in the description of growth time-profiles. For single cell growth, because of the relatively limited data-range, the statistical evidence is not strong enough to favor one model clearly over the other and to settle the bilinear versus exponential dispute. Nevertheless, for the present individual cell growth data for fission yeast, the bilinear model seems more adequate according to all metrics, especially in the case of wee1Δ cells.
Elsheikh, A. H.
2013-12-01
Calibration of subsurface flow models is an essential step for managing ground water aquifers, designing of contaminant remediation plans, and maximizing recovery from hydrocarbon reservoirs. We investigate an efficient sampling algorithm known as nested sampling (NS), which can simultaneously sample the posterior distribution for uncertainty quantification, and estimate the Bayesian evidence for model selection. Model selection statistics, such as the Bayesian evidence, are needed to choose or assign different weights to different models of different levels of complexities. In this work, we report the first successful application of nested sampling for calibration of several nonlinear subsurface flow problems. The estimated Bayesian evidence by the NS algorithm is used to weight different parameterizations of the subsurface flow models (prior model selection). The results of the numerical evaluation implicitly enforced Occam\\'s razor where simpler models with fewer number of parameters are favored over complex models. The proper level of model complexity was automatically determined based on the information content of the calibration data and the data mismatch of the calibrated model.
The Continual Reassessment Method for Multiple Toxicity Grades: A Bayesian Model Selection Approach
Yuan, Ying; Zhang, Shemin; Zhang, Wenhong; Li, Chanjuan; Wang, Ling; Xia, Jielai
2014-01-01
Grade information has been considered in Yuan et al. (2007) wherein they proposed a Quasi-CRM method to incorporate the grade toxicity information in phase I trials. A potential problem with the Quasi-CRM model is that the choice of skeleton may dramatically vary the performance of the CRM model, which results in similar consequences for the Quasi-CRM model. In this paper, we propose a new model by utilizing bayesian model selection approach – Robust Quasi-CRM model – to tackle the above-mentioned pitfall with the Quasi-CRM model. The Robust Quasi-CRM model literally inherits the BMA-CRM model proposed by Yin and Yuan (2009) to consider a parallel of skeletons for Quasi-CRM. The superior performance of Robust Quasi-CRM model was demonstrated by extensive simulation studies. We conclude that the proposed method can be freely used in real practice. PMID:24875783
Teodoro, P E; Bhering, L L; Costa, R D; Rocha, R B; Laviola, B G
2016-08-19
The aim of this study was to estimate genetic parameters via mixed models and simultaneously to select Jatropha progenies grown in three regions of Brazil that meet high adaptability and stability. From a previous phenotypic selection, three progeny tests were installed in 2008 in the municipalities of Planaltina-DF (Midwest), Nova Porteirinha-MG (Southeast), and Pelotas-RS (South). We evaluated 18 families of half-sib in a randomized block design with three replications. Genetic parameters were estimated using restricted maximum likelihood/best linear unbiased prediction. Selection was based on the harmonic mean of the relative performance of genetic values method in three strategies considering: 1) performance in each environment (with interaction effect); 2) performance in each environment (with interaction effect); and 3) simultaneous selection for grain yield, stability and adaptability. Accuracy obtained (91%) reveals excellent experimental quality and consequently safety and credibility in the selection of superior progenies for grain yield. The gain with the selection of the best five progenies was more than 20%, regardless of the selection strategy. Thus, based on the three selection strategies used in this study, the progenies 4, 11, and 3 (selected in all environments and the mean environment and by adaptability and phenotypic stability methods) are the most suitable for growing in the three regions evaluated.
gamboostLSS: An R Package for Model Building and Variable Selection in the GAMLSS Framework
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Benjamin Hofner
2016-10-01
Full Text Available Generalized additive models for location, scale and shape are a flexible class of regression models that allow to model multiple parameters of a distribution function, such as the mean and the standard deviation, simultaneously. With the R package gamboostLSS, we provide a boosting method to fit these models. Variable selection and model choice are naturally available within this regularized regression framework. To introduce and illustrate the R package gamboostLSS and its infrastructure, we use a data set on stunted growth in India. In addition to the specification and application of the model itself, we present a variety of convenience functions, including methods for tuning parameter selection, prediction and visualization of results. The package gamboostLSS is available from the Comprehensive R Archive Network (CRAN at https://CRAN.R-project.org/package=gamboostLSS.
Bayesian model selection without evidences: application to the dark energy equation-of-state
Hee, S.; Handley, W. J.; Hobson, M. P.; Lasenby, A. N.
2016-01-01
A method is presented for Bayesian model selection without explicitly computing evidences, by using a combined likelihood and introducing an integer model selection parameter n so that Bayes factors, or more generally posterior odds ratios, may be read off directly from the posterior of n. If the total number of models under consideration is specified a priori, the full joint parameter space (θ, n) of the models is of fixed dimensionality and can be explored using standard Markov chain Monte Carlo (MCMC) or nested sampling methods, without the need for reversible jump MCMC techniques. The posterior on n is then obtained by straightforward marginalization. We demonstrate the efficacy of our approach by application to several toy models. We then apply it to constraining the dark energy equation of state using a free-form reconstruction technique. We show that Λ cold dark matter is significantly favoured over all extensions, including the simple w(z) = constant model.
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Rytz Andreas
2002-06-01
Full Text Available Abstract Background The biomedical community is developing new methods of data analysis to more efficiently process the massive data sets produced by microarray experiments. Systematic and global mathematical approaches that can be readily applied to a large number of experimental designs become fundamental to correctly handle the otherwise overwhelming data sets. Results The gene selection model presented herein is based on the observation that: (1 variance of gene expression is a function of absolute expression; (2 one can model this relationship in order to set an appropriate lower fold change limit of significance; and (3 this relationship defines a function that can be used to select differentially expressed genes. The model first evaluates fold change (FC across the entire range of absolute expression levels for any number of experimental conditions. Genes are systematically binned, and those genes within the top X% of highest FCs for each bin are evaluated both with and without the use of replicates. A function is fitted through the top X% of each bin, thereby defining a limit fold change. All genes selected by the 5% FC model lie above measurement variability using a within standard deviation (SDwithin confidence level of 99.9%. Real time-PCR (RT-PCR analysis demonstrated 85.7% concordance with microarray data selected by the limit function. Conclusion The FC model can confidently select differentially expressed genes as corroborated by variance data and RT-PCR. The simplicity of the overall process permits selecting model limits that best describe experimental data by extracting information on gene expression patterns across the range of expression levels. Genes selected by this process can be consistently compared between experiments and enables the user to globally extract information with a high degree of confidence.
The selection pressures induced non-smooth infectious disease model and bifurcation analysis
International Nuclear Information System (INIS)
Qin, Wenjie; Tang, Sanyi
2014-01-01
Highlights: • A non-smooth infectious disease model to describe selection pressure is developed. • The effect of selection pressure on infectious disease transmission is addressed. • The key factors which are related to the threshold value are determined. • The stabilities and bifurcations of model have been revealed in more detail. • Strategies for the prevention of emerging infectious disease are proposed. - Abstract: Mathematical models can assist in the design strategies to control emerging infectious disease. This paper deduces a non-smooth infectious disease model induced by selection pressures. Analysis of this model reveals rich dynamics including local, global stability of equilibria and local sliding bifurcations. Model solutions ultimately stabilize at either one real equilibrium or the pseudo-equilibrium on the switching surface of the present model, depending on the threshold value determined by some related parameters. Our main results show that reducing the threshold value to a appropriate level could contribute to the efficacy on prevention and treatment of emerging infectious disease, which indicates that the selection pressures can be beneficial to prevent the emerging infectious disease under medical resource limitation
Predictive and Descriptive CoMFA Models: The Effect of Variable Selection.
Sepehri, Bakhtyar; Omidikia, Nematollah; Kompany-Zareh, Mohsen; Ghavami, Raouf
2018-01-01
Aims & Scope: In this research, 8 variable selection approaches were used to investigate the effect of variable selection on the predictive power and stability of CoMFA models. Three data sets including 36 EPAC antagonists, 79 CD38 inhibitors and 57 ATAD2 bromodomain inhibitors were modelled by CoMFA. First of all, for all three data sets, CoMFA models with all CoMFA descriptors were created then by applying each variable selection method a new CoMFA model was developed so for each data set, 9 CoMFA models were built. Obtained results show noisy and uninformative variables affect CoMFA results. Based on created models, applying 5 variable selection approaches including FFD, SRD-FFD, IVE-PLS, SRD-UVEPLS and SPA-jackknife increases the predictive power and stability of CoMFA models significantly. Among them, SPA-jackknife removes most of the variables while FFD retains most of them. FFD and IVE-PLS are time consuming process while SRD-FFD and SRD-UVE-PLS run need to few seconds. Also applying FFD, SRD-FFD, IVE-PLS, SRD-UVE-PLS protect CoMFA countor maps information for both fields. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.
Unsupervised parsing of gaze data with a beta-process vector auto-regressive hidden Markov model.
Houpt, Joseph W; Frame, Mary E; Blaha, Leslie M
2017-10-26
The first stage of analyzing eye-tracking data is commonly to code the data into sequences of fixations and saccades. This process is usually automated using simple, predetermined rules for classifying ranges of the time series into events, such as "if the dispersion of gaze samples is lower than a particular threshold, then code as a fixation; otherwise code as a saccade." More recent approaches incorporate additional eye-movement categories in automated parsing algorithms by using time-varying, data-driven thresholds. We describe an alternative approach using the beta-process vector auto-regressive hidden Markov model (BP-AR-HMM). The BP-AR-HMM offers two main advantages over existing frameworks. First, it provides a statistical model for eye-movement classification rather than a single estimate. Second, the BP-AR-HMM uses a latent process to model the number and nature of the types of eye movements and hence is not constrained to predetermined categories. We applied the BP-AR-HMM both to high-sampling rate gaze data from Andersson et al. (Behavior Research Methods 49(2), 1-22 2016) and to low-sampling rate data from the DIEM project (Mital et al., Cognitive Computation 3(1), 5-24 2011). Driven by the data properties, the BP-AR-HMM identified over five categories of movements, some which clearly mapped on to fixations and saccades, and others potentially captured post-saccadic oscillations, smooth pursuit, and various recording errors. The BP-AR-HMM serves as an effective algorithm for data-driven event parsing alone or as an initial step in exploring the characteristics of gaze data sets.
SnIPRE: selection inference using a Poisson random effects model.
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Kirsten E Eilertson
Full Text Available We present an approach for identifying genes under natural selection using polymorphism and divergence data from synonymous and non-synonymous sites within genes. A generalized linear mixed model is used to model the genome-wide variability among categories of mutations and estimate its functional consequence. We demonstrate how the model's estimated fixed and random effects can be used to identify genes under selection. The parameter estimates from our generalized linear model can be transformed to yield population genetic parameter estimates for quantities including the average selection coefficient for new mutations at a locus, the synonymous and non-synynomous mutation rates, and species divergence times. Furthermore, our approach incorporates stochastic variation due to the evolutionary process and can be fit using standard statistical software. The model is fit in both the empirical Bayes and Bayesian settings using the lme4 package in R, and Markov chain Monte Carlo methods in WinBUGS. Using simulated data we compare our method to existing approaches for detecting genes under selection: the McDonald-Kreitman test, and two versions of the Poisson random field based method MKprf. Overall, we find our method universally outperforms existing methods for detecting genes subject to selection using polymorphism and divergence data.
Modeling of Clostridium tyrobutyricum for Butyric Acid Selectivity in Continuous Fermentation
Du, Jianjun; McGraw, Amy; Hestekin, Jamie
2014-01-01
A mathematical model was developed to describe batch and continuous fermentation of glucose to organic acids with Clostridium tyrobutyricum. A modified Monod equation was used to describe cell growth, and a Luedeking-Piret equation was used to describe the production of butyric and acetic acids. Using the batch fermentation equations, models predicting butyric acid selectivity for continuous fermentation were also developed. The model showed that butyric acid production was a strong function ...
Modeling of Clostridium t yrobutyricum for Butyric Acid Selectivity in Continuous Fermentation
Jianjun Du; Amy McGraw; Jamie A. Hestekin
2014-01-01
A mathematical model was developed to describe batch and continuous fermentation of glucose to organic acids with Clostridium tyrobutyricum . A modified Monod equation was used to describe cell growth, and a Luedeking-Piret equation was used to describe the production of butyric and acetic acids. Using the batch fermentation equations, models predicting butyric acid selectivity for continuous fermentation were also developed. The model showed that butyric acid production was a strong function...
Sensor selection of helicopter transmission systems based on physical model and sensitivity analysis
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Lyu Kehong
2014-06-01
Full Text Available In the helicopter transmission systems, it is important to monitor and track the tooth damage evolution using lots of sensors and detection methods. This paper develops a novel approach for sensor selection based on physical model and sensitivity analysis. Firstly, a physical model of tooth damage and mesh stiffness is built. Secondly, some effective condition indicators (CIs are presented, and the optimal CIs set is selected by comparing their test statistics according to Mann–Kendall test. Afterwards, the selected CIs are used to generate a health indicator (HI through sen slop estimator. Then, the sensors are selected according to the monotonic relevance and sensitivity to the damage levels. Finally, the proposed method is verified by the simulation and experimental data. The results show that the approach can provide a guide for health monitoring of helicopter transmission systems, and it is effective to reduce the test cost and improve the system’s reliability.
Communities ready for takeoffIntegrating social assets for biofuel site-selection modeling.
Rijkhoff, Sanne A M; Hoard, Season A; Gaffney, Michael J; Smith, Paul M
2017-01-01
Although much of the social science literature supports the importance of community assets for success in many policy areas, these assets are often overlooked when selecting communities for new infrastructure facilities. Extensive collaboration is crucial for the success of environmental and economic projects, yet it often is not adequately addressed when making siting decisions for new projects. This article develops a social asset framework that includes social, creative, and human capital to inform site-selection decisions. This framework is applied to the Northwest Advanced Renewables Alliance project to assess community suitability for biofuel-related developments. This framework is the first to take all necessary community assets into account, providing insight into successful site selection beyond current models. The framework not only serves as a model for future biorefinery projects but also guides tasks that depend on informed location selection for success.
Vendor selection and order allocation using an integrated fuzzy mathematical programming model
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Farzaneh Talebi
2015-09-01
Full Text Available In the context of supply chain management, supplier selection plays a key role in reaching desirable production planning. In today's competitive world, many enterprises have focused on selecting the appropriate suppliers in an attempt to reduce purchasing costs and improve quality products and services. Supplier selection is a multi-criteria decision problem, which includes different qualitative and quantitative criteria such as purchase cost, on time delivery, quality of service, etc. In this study, a fuzzy multi-objective mathematical programming model is presented to select appropriate supplier and assign desirable order to different supplies. The proposed model was implemented for an organization by considering 16 different scenarios and the results are compared with two other existing methods.
The MCDM Model for Personnel Selection Based on SWARA and ARAS Methods
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Darjan Karabasevic
2015-05-01
Full Text Available Competent employees are the key resource in an organization for achieving success and, therefore, competitiveness on the market. The aim of the recruitment and selection process is to acquire personnel with certain competencies required for a particular position, i.e.,a position within the company. Bearing in mind the fact that in the process of decision making decision-makers have underused the methods of making decisions, this paper aims to establish an MCDM model for the evaluation and selection of candidates in the process of the recruitment and selection of personnel based on the SWARA and the ARAS methods. Apart from providing an MCDM model, the paper will additionally provide a set of evaluation criteria for the position of a sales manager (the middle management in the telecommunication industry which will also be used in the numerical example. On the basis of a numerical example, in the process of employment, theproposed MCDMmodel can be successfully usedin selecting candidates.
Hidden Markov modeling of frequency-following responses to Mandarin lexical tones.
Llanos, Fernando; Xie, Zilong; Chandrasekaran, Bharath
2017-11-01
The frequency-following response (FFR) is a scalp-recorded electrophysiological potential reflecting phase-locked activity from neural ensembles in the auditory system. The FFR is often used to assess the robustness of subcortical pitch processing. Due to low signal-to-noise ratio at the single-trial level, FFRs are typically averaged across thousands of stimulus repetitions. Prior work using this approach has shown that subcortical encoding of linguistically-relevant pitch patterns is modulated by long-term language experience. We examine the extent to which a machine learning approach using hidden Markov modeling (HMM) can be utilized to decode Mandarin tone-categories from scalp-record electrophysiolgical activity. We then assess the extent to which the HMM can capture biologically-relevant effects (language experience-driven plasticity). To this end, we recorded FFRs to four Mandarin tones from 14 adult native speakers of Chinese and 14 of native English. We trained a HMM to decode tone categories from the FFRs with varying size of averages. Tone categories were decoded with above-chance accuracies using HMM. The HMM derived metric (decoding accuracy) revealed a robust effect of language experience, such that FFRs from native Chinese speakers yielded greater accuracies than native English speakers. Critically, the language experience-driven plasticity was captured with average sizes significantly smaller than those used in the extant literature. Our results demonstrate the feasibility of HMM in assessing the robustness of neural pitch. Machine-learning approaches can complement extant analytical methods that capture auditory function and could reduce the number of trials needed to capture biological phenomena. Copyright © 2017 Elsevier B.V. All rights reserved.
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Masoud Ghodrati
Full Text Available Humans can effectively and swiftly recognize objects in complex natural scenes. This outstanding ability has motivated many computational object recognition models. Most of these models try to emulate the behavior of this remarkable system. The human visual system hierarchically recognizes objects in several processing stages. Along these stages a set of features with increasing complexity is extracted by different parts of visual system. Elementary features like bars and edges are processed in earlier levels of visual pathway and as far as one goes upper in this pathway more complex features will be spotted. It is an important interrogation in the field of visual processing to see which features of an object are selected and represented by the visual cortex. To address this issue, we extended a hierarchical model, which is motivated by biology, for different object recognition tasks. In this model, a set of object parts, named patches, extracted in the intermediate stages. These object parts are used for training procedure in the model and have an important role in object recognition. These patches are selected indiscriminately from different positions of an image and this can lead to the extraction of non-discriminating patches which eventually may reduce the performance. In the proposed model we used an evolutionary algorithm approach to select a set of informative patches. Our reported results indicate that these patches are more informative than usual random patches. We demonstrate the strength of the proposed model on a range of object recognition tasks. The proposed model outperforms the original model in diverse object recognition tasks. It can be seen from the experiments that selected features are generally particular parts of target images. Our results suggest that selected features which are parts of target objects provide an efficient set for robust object recognition.
Bayesian model selection: Evidence estimation based on DREAM simulation and bridge sampling
Volpi, Elena; Schoups, Gerrit; Firmani, Giovanni; Vrugt, Jasper A.
2017-04-01
Bayesian inference has found widespread application in Earth and Environmental Systems Modeling, providing an effective tool for prediction, data assimilation, parameter estimation, uncertainty analysis and hypothesis testing. Under multiple competing hypotheses, the Bayesian approach also provides an attractive alternative to traditional information criteria (e.g. AIC, BIC) for model selection. The key variable for Bayesian model selection is the evidence (or marginal likelihood) that is the normalizing constant in the denominator of Bayes theorem; while it is fundamental for model selection, the evidence is not required for Bayesian inference. It is computed for each hypothesis (model) by averaging the likelihood function over the prior parameter distribution, rather than maximizing it as by information criteria; the larger a model evidence the more support it receives among a collection of hypothesis as the simulated values assign relatively high probability density to the observed data. Hence, the evidence naturally acts as an Occam's razor, preferring simpler and more constrained models against the selection of over-fitted ones by information criteria that incorporate only the likelihood maximum. Since it is not particularly easy to estimate the evidence in practice, Bayesian model selection via the marginal likelihood has not yet found mainstream use. We illustrate here the properties of a new estimator of the Bayesian model evidence, which provides robust and unbiased estimates of the marginal likelihood; the method is coined Gaussian Mixture Importance Sampling (GMIS). GMIS uses multidimensional numerical integration of the posterior parameter distribution via bridge sampling (a generalization of importance sampling) of a mixture distribution fitted to samples of the posterior distribution derived from the DREAM algorithm (Vrugt et al., 2008; 2009). Some illustrative examples are presented to show the robustness and superiority of the GMIS estimator with
Libiseller, Claudia; Grimvall, Anders
Meteorological normalisation of time series of air quality data aims to extract anthropogenic signals by removing natural fluctuations in the collected data. We showed that the currently used procedures to select normalisation models can cause over-fitting to observed data and undesirable smoothing of anthropogenic signals. A simulation study revealed that the risk of such effects is particularly large when: (i) the observed data are serially correlated, (ii) the normalisation model is selected by leave-one-out cross-validation, and (iii) complex models, such as artificial neural networks, are fitted to data. When the size of the test sets used in the cross-validation was increased, and only moderately complex linear models were fitted to data, the over-fitting was less pronounced. An empirical study of the predictive ability of different normalisation models for tropospheric ozone in Finland confirmed the importance of using appropriate model selection strategies. Moderately complex regional models involving contemporaneous meteorological data from a network of stations were found to be superior to single-site models as well as more complex regional models involving both contemporaneous and time-lagged meteorological data from a network of stations.
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Yoshihiro eUesawa
2016-02-01
Full Text Available Random forest (RF is a machine-learning ensemble method with high predictive performance. Majority voting in RF uses the discrimination results in numerous decision trees produced from bootstrapping data. For the same dataset, the bootstrapping process yields different predictive capacities in each generation. As participants in the Toxicology in the 21st Century (Tox21 DATA Challenge 2014, we produced numerous RF models for predicting the structures of compounds that can activate each toxicity-related pathway, and then selected the model with the highest predictive ability. Half of the compounds in the training dataset supplied by the competition organizer were allocated to the validation dataset. The remaining compounds were used in model construction. The charged and uncharged forms of each molecule were calculated using the molecular operating environment (MOE software. Subsequently, the descriptors were computed using MOE, MarvinView, and Dragon. These combined methods yielded over 4,071 descriptors for model construction. Using these descriptors, pattern recognition analyses were performed by RF implemented in JMP Pro (a statistical software package. A hundred to two hundred RF models were generated for each pathway. The predictive performance of each model was tested against the validation dataset, and the best-performing model was selected. In the competition, the latter model selected a best-performing model from the 50% test set that best predicted the structures of compounds that activate the estrogen receptor ligand-binding domain (ER-LBD.
Nallikuzhy, Jiss J; Dandapat, S
2017-06-01
In this work, a new patient-specific approach to enhance the spatial resolution of ECG is proposed and evaluated. The proposed model transforms a three-lead ECG into a standard twelve-lead ECG thereby enhancing its spatial resolution. The three leads used for prediction are obtained from the standard twelve-lead ECG. The proposed model takes advantage of the improved inter-lead correlation in wavelet domain. Since the model is patient-specific, it also selects the optimal predictor leads for a given patient using a lead selection algorithm. The lead selection algorithm is based on a new diagnostic similarity score which computes the diagnostic closeness between the original and the spatially enhanced leads. Standard closeness measures are used to assess the performance of the model. The similarity in diagnostic information between the original and the spatially enhanced leads are evaluated using various diagnostic measures. Repeatability and diagnosability are performed to quantify the applicability of the model. A comparison of the proposed model is performed with existing models that transform a subset of standard twelve-lead ECG into the standard twelve-lead ECG. From the analysis of the results, it is evident that the proposed model preserves diagnostic information better compared to other models. Copyright © 2017 Elsevier Ltd. All rights reserved.
International Nuclear Information System (INIS)
Miller, C.W.; Dunning, D.E. Jr.; Etnier, E.L.; Hoffman, F.O.; Little, C.A.; Meyer, H.R.; Shaeffer, D.L.; Till, J.E.
1979-07-01
Evaluations of selected predictive models and parameters used in the assessment of the environmental transport and dosimetry of radionuclides are summarized. Mator sections of this report include a validation of the Gaussian plume disperson model, comparison of the output of a model for the transport of 131 I from vegetation to milk with field data, validation of a model for the fraction of aerosols intercepted by vegetation, an evaluation of dose conversion factors for 232 Th, an evaluation of considering the effect of age dependency on population dose estimates, and a summary of validation results for hydrologic transport models
Energy Technology Data Exchange (ETDEWEB)
Miller, C.W.; Dunning, D.E. Jr.; Etnier, E.L.; Hoffman, F.O.; Little, C.A.; Meyer, H.R.; Shaeffer, D.L.; Till, J.E.
1979-07-01
Evaluations of selected predictive models and parameters used in the assessment of the environmental transport and dosimetry of radionuclides are summarized. Mator sections of this report include a validation of the Gaussian plume disperson model, comparison of the output of a model for the transport of /sup 131/I from vegetation to milk with field data, validation of a model for the fraction of aerosols intercepted by vegetation, an evaluation of dose conversion factors for /sup 232/Th, an evaluation of considering the effect of age dependency on population dose estimates, and a summary of validation results for hydrologic transport models.
Jun, Gyuchan T; Morris, Zoe; Eldabi, Tillal; Harper, Paul; Naseer, Aisha; Patel, Brijesh; Clarkson, John P
2011-05-19
There is an increasing recognition that modelling and simulation can assist in the process of designing health care policies, strategies and operations. However, the current use is limited and answers to questions such as what methods to use and when remain somewhat underdeveloped. The aim of this study is to provide a mechanism for decision makers in health services planning and management to compare a broad range of modelling and simulation methods so that they can better select and use them or better commission relevant modelling and simulation work. This paper proposes a modelling and simulation method comparison and selection tool developed from a comprehensive literature review, the research team's extensive expertise and inputs from potential users. Twenty-eight different methods were identified, characterised by their relevance to different application areas, project life cycle stages, types of output and levels of insight, and four input resources required (time, money, knowledge and data). The characterisation is presented in matrix forms to allow quick comparison and selection. This paper also highlights significant knowledge gaps in the existing literature when assessing the applicability of particular approaches to health services management, where modelling and simulation skills are scarce let alone money and time. A modelling and simulation method comparison and selection tool is developed to assist with the selection of methods appropriate to supporting specific decision making processes. In particular it addresses the issue of which method is most appropriate to which specific health services management problem, what the user might expect to be obtained from the method, and what is required to use the method. In summary, we believe the tool adds value to the scarce existing literature on methods comparison and selection.
Archer, C Ruth; Hunt, John
2015-11-01
Aging evolved because the strength of natural selection declines over the lifetime of most organisms. Weak natural selection late in life allows the accumulation of deleterious mutations and may favor alleles that have positive effects on fitness early in life, but costly pleiotropic effects expressed later on. While this decline in natural selection is central to longstanding evolutionary explanations for aging, a role for sexual selection and sexual conflict in the evolution of lifespan and aging has only been identified recently. Testing how sexual selection and sexual conflict affect lifespan and aging is challenging as it requires quantifying male age-dependent reproductive success. This is difficult in the invertebrate model organisms traditionally used in aging research. Research using crickets (Orthoptera: Gryllidae), where reproductive investment can be easily measured in both sexes, has offered exciting and novel insights into how sexual selection and sexual conflict affect the evolution of aging, both in the laboratory and in the wild. Here we discuss how sexual selection and sexual conflict can be integrated alongside evolutionary and mechanistic theories of aging using crickets as a model. We then highlight the potential for research using crickets to further advance our understanding of lifespan and aging. Copyright © 2015 Elsevier Inc. All rights reserved.
Directory of Open Access Journals (Sweden)
Branka Marasović
2009-03-01
Full Text Available In this paper we select an optimal portfolio on the Croatian capital market by using the multicriterial programming. In accordance with the modern portfolio theory maximisation of returns at minimal risk should be the investment goal of any successful investor. However, contrary to the expectations of the modern portfolio theory, the tests carried out on a number of financial markets reveal the existence of other indicators important in portfolio selection. Considering the importance of variables other than return and risk, selection of the optimal portfolio becomes a multicriterial problem which should be solved by using the appropriate techniques.In order to select an optimal portfolio, absolute values of criteria, like return, risk, price to earning value ratio (P/E, price to book value ratio (P/B and price to sale value ratio (P/S are included in our multicriterial model. However the problem might occur as the mean values of some criteria are significantly different for different sectors and because financial managers emphasize that comparison of the same criteria for different sectors could lead us to wrong conclusions. In the second part of the paper, relative values of previously stated criteria (in relation to mean value of sector are included in model for selecting optimal portfolio. Furthermore, the paper shows that if relative values of criteria are included in multicriterial model for selecting optimal portfolio, return in subsequent period is considerably higher than if absolute values of the same criteria were used.
Lubke, Gitta H.; Campbell, Ian
2016-01-01
Inference and conclusions drawn from model fitting analyses are commonly based on a single “best-fitting” model. If model selection and inference are carried out using the same data model selection uncertainty is ignored. We illustrate the Type I error inflation that can result from using the same data for model selection and inference, and we then propose a simple bootstrap based approach to quantify model selection uncertainty in terms of model selection rates. A selection rate can be interpreted as an estimate of the replication probability of a fitted model. The benefits of bootstrapping model selection uncertainty is demonstrated in a growth mixture analyses of data from the National Longitudinal Study of Youth, and a 2-group measurement invariance analysis of the Holzinger-Swineford data. PMID:28663687
Wöhling, T.; Schöniger, A.; Geiges, A.; Nowak, W.; Gayler, S.
2013-12-01
The objective selection of appropriate models for realistic simulations of coupled soil-plant processes is a challenging task since the processes are complex, not fully understood at larger scales, and highly non-linear. Also, comprehensive data sets are scarce, and measurements are uncertain. In the past decades, a variety of different models have been developed that exhibit a wide range of complexity regarding their approximation of processes in the coupled model compartments. We present a method for evaluating experimental design for maximum confidence in the model selection task. The method considers uncertainty in parameters, measurements and model structures. Advancing the ideas behind Bayesian Model Averaging (BMA), we analyze the changes in posterior model weights and posterior model choice uncertainty when more data are made available. This allows assessing the power of different data types, data densities and data locations in identifying the best model structure from among a suite of plausible models. The models considered in this study are the crop models CERES, SUCROS, GECROS and SPASS, which are coupled to identical routines for simulating soil processes within the modelling framework Expert-N. The four models considerably differ in the degree of detail at which crop growth and root water uptake are represented. Monte-Carlo simulations were conducted for each of these models considering their uncertainty in soil hydraulic properties and selected crop model parameters. Using a Bootstrap Filter (BF), the models were then conditioned on field measurements of soil moisture, matric potential, leaf-area index, and evapotranspiration rates (from eddy-covariance measurements) during a vegetation period of winter wheat at a field site at the Swabian Alb in Southwestern Germany. Following our new method, we derived model weights when using all data or different subsets thereof. We discuss to which degree the posterior mean outperforms the prior mean and all
Automating an integrated spatial data-mining model for landfill site selection
Abujayyab, Sohaib K. M.; Ahamad, Mohd Sanusi S.; Yahya, Ahmad Shukri; Ahmad, Siti Zubaidah; Aziz, Hamidi Abdul
2017-10-01
An integrated programming environment represents a robust approach to building a valid model for landfill site selection. One of the main challenges in the integrated model is the complicated processing and modelling due to the programming stages and several limitations. An automation process helps avoid the limitations and improve the interoperability between integrated programming environments. This work targets the automation of a spatial data-mining model for landfill site selection by integrating between spatial programming environment (Python-ArcGIS) and non-spatial environment (MATLAB). The model was constructed using neural networks and is divided into nine stages distributed between Matlab and Python-ArcGIS. A case study was taken from the north part of Peninsular Malaysia. 22 criteria were selected to utilise as input data and to build the training and testing datasets. The outcomes show a high-performance accuracy percentage of 98.2% in the testing dataset using 10-fold cross validation. The automated spatial data mining model provides a solid platform for decision makers to performing landfill site selection and planning operations on a regional scale.
Directory of Open Access Journals (Sweden)
Kang-Wook Lee
2017-05-01
Full Text Available An important issue for international businesses and academia is selecting countries in which to expand in order to achieve entrepreneurial sustainability. This study develops a country selection model for sustainable construction businesses using both objective and subjective information. The objective information consists of 14 variables related to country risk and project performance in 32 countries over 25 years. This hybrid model applies subjective weighting from industrial experts to objective information using a fuzzy LinPreRa-based Analytic Hierarchy Process. The hybrid model yields a more accurate country selection compared to a purely objective information-based model in experienced countries. Interestingly, the hybrid model provides some different predictions with only subjective opinions in unexperienced countries, which implies that expert opinion is not always reliable. In addition, feedback from five experts in top international companies is used to validate the model’s completeness, effectiveness, generality, and applicability. The model is expected to aid decision makers in selecting better candidate countries that lead to sustainable business success.
A Time-Series Water Level Forecasting Model Based on Imputation and Variable Selection Method
Directory of Open Access Journals (Sweden)
Jun-He Yang
2017-01-01
Full Text Available Reservoirs are important for households and impact the national economy. This paper proposed a time-series forecasting model based on estimating a missing value followed by variable selection to forecast the reservoir’s water level. This study collected data from the Taiwan Shimen Reservoir as well as daily atmospheric data from 2008 to 2015. The two datasets are concatenated into an integrated dataset based on ordering of the data as a research dataset. The proposed time-series forecasting model summarily has three foci. First, this study uses five imputation methods to directly delete the missing value. Second, we identified the key variable via factor analysis and then deleted the unimportant variables sequentially via the variable selection method. Finally, the proposed model uses a Random Forest to build the forecasting model of the reservoir’s water level. This was done to compare with the listing method under the forecasting error. These experimental results indicate that the Random Forest forecasting model when applied to variable selection with full variables has better forecasting performance than the listing model. In addition, this experiment shows that the proposed variable selection can help determine five forecast methods used here to improve the forecasting capability.
A Site Selection Model for a Straw-Based Power Generation Plant with CO2 Emissions
Directory of Open Access Journals (Sweden)
Hao Lv
2014-10-01
Full Text Available The decision on the location of a straw-based power generation plant has a great influence on the plant’s operation and performance. This study explores traditional theories for site selection. Using integer programming, the study optimizes the economic and carbon emission outcomes of straw-based power generation as two objectives, with the supply and demand of straw as constraints. It provides a multi-objective mixed-integer programming model to solve the site selection problem for a straw-based power generation plant. It then provides a case study to demonstrate the application of the model in the decision on the site selection for a straw-based power generation plant with a Chinese region. Finally, the paper discusses the result of the model in the context of the wider aspect of straw-based power generation.
Tsoi, B; O'Reilly, D; Jegathisawaran, J; Tarride, J-E; Blackhouse, G; Goeree, R
2015-06-17
In constructing or appraising a health economic model, an early consideration is whether the modelling approach selected is appropriate for the given decision problem. Frameworks and taxonomies that distinguish between modelling approaches can help make this decision more systematic and this study aims to identify and compare the decision frameworks proposed to date on this topic area. A systematic review was conducted to identify frameworks from peer-reviewed and grey literature sources. The following databases were searched: OVID Medline and EMBASE; Wiley's Cochrane Library and Health Economic Evaluation Database; PubMed; and ProQuest. Eight decision frameworks were identified, each focused on a different set of modelling approaches and employing a different collection of selection criterion. The selection criteria can be categorized as either: (i) structural features (i.e. technical elements that are factual in nature) or (ii) practical considerations (i.e. context-dependent attributes). The most commonly mentioned structural features were population resolution (i.e. aggregate vs. individual) and interactivity (i.e. static vs. dynamic). Furthermore, understanding the needs of the end-users and stakeholders was frequently incorporated as a criterion within these frameworks. There is presently no universally-accepted framework for selecting an economic modelling approach. Rather, each highlights different criteria that may be of importance when determining whether a modelling approach is appropriate. Further discussion is thus necessary as the modelling approach selected will impact the validity of the underlying economic model and have downstream implications on its efficiency, transparency and relevance to decision-makers.
Genomic Selection Accuracy using Multifamily Prediction Models in a Wheat Breeding Program
Directory of Open Access Journals (Sweden)
Elliot L. Heffner
2011-03-01
Full Text Available Genomic selection (GS uses genome-wide molecular marker data to predict the genetic value of selection candidates in breeding programs. In plant breeding, the ability to produce large numbers of progeny per cross allows GS to be conducted within each family. However, this approach requires phenotypes of lines from each cross before conducting GS. This will prolong the selection cycle and may result in lower gains per year than approaches that estimate marker-effects with multiple families from previous selection cycles. In this study, phenotypic selection (PS, conventional marker-assisted selection (MAS, and GS prediction accuracy were compared for 13 agronomic traits in a population of 374 winter wheat ( L. advanced-cycle breeding lines. A cross-validation approach that trained and validated prediction accuracy across years was used to evaluate effects of model selection, training population size, and marker density in the presence of genotype × environment interactions (G×E. The average prediction accuracies using GS were 28% greater than with MAS and were 95% as accurate as PS. For net merit, the average accuracy across six selection indices for GS was 14% greater than for PS. These results provide empirical evidence that multifamily GS could increase genetic gain per unit time and cost in plant breeding.
Ye, M.; Elshall, A. S.; Tang, G.; Samani, S.
2016-12-01
Bayesian Model Evidence (BME) is the measure of the average fit of the model to data given all the parameter values that the model can take. By accounting for the trade-off between the model ability to reproduce the observation data and model complexity, BME estimates of candidate models are employed to calculate model weights, which are used for model selection and model averaging. This study shows that accurate estimation of the BME is important for penalizing models with more complexity. To improve the accuracy of BME estimation, we resort to Monte Carlo numerical estimators over semi-analytical solutions (such as Laplace approximations, BIC, KIC and other). This study examines prominent numerical estimators of BME that are the thermodynamic integration (TI), and the importance sampling methods of arithmetic mean (AM), harmonic mean (HM), and steppingstone sampling (SS). AM estimator (based on prior sampling) and HM estimator (based on posterior sampling) are straightforward to implement, yet they lead to under and over estimation, respectively. TI and SS improve beyond this by means of sampling multiple intermediate distributions that links the prior and the posterior, using Markov Chain Monte Carlo (MCMC). TI and SS are theoretically unbiased estimators that are mathematically rigorous. Yet a theoretically unbiased estimator could have large bias in practice arising from numerical implementation, because MCMC sampling errors of certain intermediate distributions can introduce bias. We propose an SS variant, namely the multiple one-steppingstone sampling (MOSS), which turns these intermediate stumbling "blocks" of SS into steppingstones toward BME estimation. Thus, MOSS is less sensitive to MCMC sampling errors. We evaluate these estimators using a problem of groundwater transport model selection. The modeling results show that SS and MOSS estimators gave the most accurate results. In addition, the results show that the magnitude of the estimation error is a
PARAMETER ESTIMATION AND MODEL SELECTION FOR INDOOR ENVIRONMENTS BASED ON SPARSE OBSERVATIONS
Directory of Open Access Journals (Sweden)
Y. Dehbi
2017-09-01
Full Text Available This paper presents a novel method for the parameter estimation and model selection for the reconstruction of indoor environments based on sparse observations. While most approaches for the reconstruction of indoor models rely on dense observations, we predict scenes of the interior with high accuracy in the absence of indoor measurements. We use a model-based top-down approach and incorporate strong but profound prior knowledge. The latter includes probability density functions for model parameters and sparse observations such as room areas and the building footprint. The floorplan model is characterized by linear and bi-linear relations with discrete and continuous parameters. We focus on the stochastic estimation of model parameters based on a topological model derived by combinatorial reasoning in a first step. A Gauss-Markov model is applied for estimation and simulation of the model parameters. Symmetries are represented and exploited during the estimation process. Background knowledge as well as observations are incorporated in a maximum likelihood estimation and model selection is performed with AIC/BIC. The likelihood is also used for the detection and correction of potential errors in the topological model. Estimation results are presented and discussed.
Parameter Estimation and Model Selection for Indoor Environments Based on Sparse Observations
Dehbi, Y.; Loch-Dehbi, S.; Plümer, L.
2017-09-01
This paper presents a novel method for the parameter estimation and model selection for the reconstruction of indoor environments based on sparse observations. While most approaches for the reconstruction of indoor models rely on dense observations, we predict scenes of the interior with high accuracy in the absence of indoor measurements. We use a model-based top-down approach and incorporate strong but profound prior knowledge. The latter includes probability density functions for model parameters and sparse observations such as room areas and the building footprint. The floorplan model is characterized by linear and bi-linear relations with discrete and continuous parameters. We focus on the stochastic estimation of model parameters based on a topological model derived by combinatorial reasoning in a first step. A Gauss-Markov model is applied for estimation and simulation of the model parameters. Symmetries are represented and exploited during the estimation process. Background knowledge as well as observations are incorporated in a maximum likelihood estimation and model selection is performed with AIC/BIC. The likelihood is also used for the detection and correction of potential errors in the topological model. Estimation results are presented and discussed.
A multicriteria decision making model for assessment and selection of an ERP in a logistics context
Pereira, Teresa; Ferreira, Fernanda A.
2017-07-01
The aim of this work is to apply a methodology of decision support based on a multicriteria decision analyses (MCDA) model that allows the assessment and selection of an Enterprise Resource Planning (ERP) in a Portuguese logistics company by Group Decision Maker (GDM). A Decision Support system (DSS) that implements a MCDA - Multicriteria Methodology for the Assessment and Selection of Information Systems / Information Technologies (MMASSI / IT) is used based on its features and facility to change and adapt the model to a given scope. Using this DSS it was obtained the information system that best suited to the decisional context, being this result evaluated through a sensitivity and robustness analysis.
Influence of Selective Edge Removal and Refractory Period in a Self-Organized Critical Neuron Model
International Nuclear Information System (INIS)
Lin Min; Gang, Zhao; Chen Tianlun
2009-01-01
A simple model for a set of integrate-and-fire neurons based on the weighted network is introduced. By considering the neurobiological phenomenon in brain development and the difference of the synaptic strength, we construct weighted networks develop with link additions and followed by selective edge removal. The network exhibits the small-world and scale-free properties with high network efficiency. The model displays an avalanche activity on a power-law distribution. We investigate the effect of selective edge removal and the neuron refractory period on the self-organized criticality of the system. (condensed matter: structural, mechanical, and thermal properties)
DEFF Research Database (Denmark)
Knudsen, Jenny Dahl; Odenholt, Inga; Erlendsdottir, Helga
2003-01-01
Pharmacokinetic (PK) and pharmacodynamic (PD) properties for the selection of resistant pneumococci were studied by using three strains of the same serotype (6B) for mixed-culture infection in time-kill experiments in vitro and in three different animal models, the mouse peritonitis, the mouse....../ml was used in the rabbit tissue cage model. During the different treatment regimens, the differences in numbers of CFU between treated and control animals were calculated to measure the efficacies of the regimens. Selective media with erythromycin or different penicillin concentrations were used to quantify...
Using the Animal Model to Accelerate Response to Selection in a Self-Pollinating Crop
Cowling, Wallace A.; Stefanova, Katia T.; Beeck, Cameron P.; Nelson, Matthew N.; Hargreaves, Bonnie L. W.; Sass, Olaf; Gilmour, Arthur R.; Siddique, Kadambot H. M.
2015-01-01
We used the animal model in S0 (F1) recurrent selection in a self-pollinating crop including, for the first time, phenotypic and relationship records from self progeny, in addition to cross progeny, in the pedigree. We tested the model in Pisum sativum, the autogamous annual species used by Mendel to demonstrate the particulate nature of inheritance. Resistance to ascochyta blight (Didymella pinodes complex) in segregating S0 cross progeny was assessed by best linear unbiased prediction over two cycles of selection. Genotypic concurrence across cycles was provided by pure-line ancestors. From cycle 1, 102/959 S0 plants were selected, and their S1 self progeny were intercrossed and selfed to produce 430 S0 and 575 S2 individuals that were evaluated in cycle 2. The analysis was improved by including all genetic relationships (with crossing and selfing in the pedigree), additive and nonadditive genetic covariances between cycles, fixed effects (cycles and spatial linear trends), and other random effects. Narrow-sense heritability for ascochyta blight resistance was 0.305 and 0.352 in cycles 1 and 2, respectively, calculated from variance components in the full model. The fitted correlation of predicted breeding values across cycles was 0.82. Average accuracy of predicted breeding values was 0.851 for S2 progeny of S1 parent plants and 0.805 for S0 progeny tested in cycle 2, and 0.878 for S1 parent plants for which no records were available. The forecasted response to selection was 11.2% in the next cycle with 20% S0 selection proportion. This is the first application of the animal model to cyclic selection in heterozygous populations of selfing plants. The method can be used in genomic selection, and for traits measured on S0-derived bulks such as grain yield. PMID:25943522
On theoretical models of gene expression evolution with random genetic drift and natural selection.
Ogasawara, Osamu; Okubo, Kousaku
2009-11-20
The relative contributions of natural selection and random genetic drift are a major source of debate in the study of gene expression evolution, which is hypothesized to serve as a bridge from molecular to phenotypic evolution. It has been suggested that the conflict between views is caused by the lack of a definite model of the neutral hypothesis, which can describe the long-run behavior of evolutionary change in mRNA abundance. Therefore previous studies have used inadequate analogies with the neutral prediction of other phenomena, such as amino acid or nucleotide sequence evolution, as the null hypothesis of their statistical inference. In this study, we introduced two novel theoretical models, one based on neutral drift and the other assuming natural selection, by focusing on a common property of the distribution of mRNA abundance among a variety of eukaryotic cells, which reflects the result of long-term evolution. Our results demonstrated that (1) our models can reproduce two independently found phenomena simultaneously: the time development of gene expression divergence and Zipf's law of the transcriptome; (2) cytological constraints can be explicitly formulated to describe long-term evolution; (3) the model assuming that natural selection optimized relative mRNA abundance was more consistent with previously published observations than the model of optimized absolute mRNA abundances. The models introduced in this study give a formulation of evolutionary change in the mRNA abundance of each gene as a stochastic process, on the basis of previously published observations. This model provides a foundation for interpreting observed data in studies of gene expression evolution, including identifying an adequate time scale for discriminating the effect of natural selection from that of random genetic drift of selectively neutral variations.
muMAB: A Multi-Armed Bandit Model for Wireless Network Selection
Directory of Open Access Journals (Sweden)
Stefano Boldrini
2018-01-01
Full Text Available Multi-armed bandit (MAB models are a viable approach to describe the problem of best wireless network selection by a multi-Radio Access Technology (multi-RAT device, with the goal of maximizing the quality perceived by the final user. The classical MAB model does not allow, however, to properly describe the problem of wireless network selection by a multi-RAT device, in which a device typically performs a set of measurements in order to collect information on available networks, before a selection takes place. The MAB model foresees in fact only one possible action for the player, which is the selection of one among different arms at each time step; existing arm selection algorithms thus mainly differ in the rule according to which a specific arm is selected. This work proposes a new MAB model, named measure-use-MAB (muMAB, aiming at providing a higher flexibility, and thus a better accuracy in describing the network selection problem. The muMAB model extends the classical MAB model in a twofold manner; first, it foresees two different actions: to measure and to use; second, it allows actions to span over multiple time steps. Two new algorithms designed to take advantage of the higher flexibility provided by the muMAB model are also introduced. The first one, referred to as measure-use-UCB1 (muUCB1 is derived from the well known UCB1 algorithm, while the second one, referred to as Measure with Logarithmic Interval (MLI, is appositely designed for the new model so to take advantage of the new measure action, while aggressively using the best arm. The new algorithms are compared against existing ones from the literature in the context of the muMAB model, by means of computer simulations using both synthetic and captured data. Results show that the performance of the algorithms heavily depends on the Probability Density Function (PDF of the reward received on each arm, with different algorithms leading to the best performance depending on the PDF
Dynamic time warping in phoneme modeling for fast pronunciation error detection.
Miodonska, Zuzanna; Bugdol, Marcin D; Krecichwost, Michal
2016-02-01
The presented paper describes a novel approach to the detection of pronunciation errors. It makes use of the modeling of well-pronounced and mispronounced phonemes by means of the Dynamic Time Warping (DTW) algorithm. Four approaches that make use of the DTW phoneme modeling were developed to detect pronunciation errors: Variations of the Word Structure (VoWS), Normalized Phoneme Distances Thresholding (NPDT), Furthest Segment Search (FSS) and Normalized Furthest Segment Search (NFSS). The performance evaluation of each module was carried out using a speech database of correctly and incorrectly pronounced words in the Polish language, with up to 10 patterns of every trained word from a set of 12 words having different phonetic structures. The performance of DTW modeling was compared to Hidden Markov Models (HMM) that were used for the same four approaches (VoWS, NPDT, FSS, NFSS). The average error rate (AER) was the lowest for DTW with NPDT (AER=0.287) and scored better than HMM with FSS (AER=0.473), which was the best result for HMM. The DTW modeling was faster than HMM for all four approaches. This technique can be used for computer-assisted pronunciation training systems that can work with a relatively small training speech corpus (less than 20 patterns per word) to support speech therapy at home. Copyright © 2015 Elsevier Ltd. All rights reserved.
Modeling of cesium sorption on biotite using cation exchange selectivity coefficients
Energy Technology Data Exchange (ETDEWEB)
Kylloenen, Jarkko; Hakanen, Martti; Harjula, Risto; Lehto, Jukka [Helsinki Univ. (Finland). Lab. of Radiochemistry; Lindberg, Antero [Geological Survey of Finland, Espoo (Finland); Vehkamaeki, Marko [Helsinki Univ. (Finland). Lab. of Inorganic Chemistry
2014-07-01
For the modeling of cesium sorption on biotite, samples of natural biotite separated from gneissic rocks were converted into monoionic potassium, sodium, and calcium forms, and sorption isotherms for Cs/K, Cs/Na and Cs/Ca exchange were determined at pH 6 and 8 in 10{sup -4}-10{sup -8} M Cs solutions. Selectivity coefficients for Cs/K, Cs/Na, and Cs/Ca ion exchange reactions were calculated from the isotherm data, using the Gaines-Thomas convention. At Cs loadings below 1% of the total ion exchange capacity, the overall selectivity coefficient for Cs/Ca exchange was approximately five and seven orders of magnitude higher than those for Cs/Na and Cs/K exchange, respectively. Based on the selectivity coefficients, the ion exchange isotherms were modeled with the U.S. Geological Survey PhreeqC program, assuming three different types of ion exchange site: sites on the basal planes on biotite crystal surfaces with 95% site abundance, probable interlayer sites on crystal edges [frayed edge sites (FESs)] (0.02%) and third-type sites (5%), the physical background of which is unclear. Of these three types, the FES sites were superior in Cs selectivity, while the planar sites exhibited the lowest selectivity, and the third-type sites had selectivity between these two. The functionality of the model was successfully verified by modeling the Cs sorption isotherms on crushed mica gneiss rock in saline groundwater. Determination of the exchangeable ions K, Na, Ca, and Cs on the basal plane and edge surfaces by scanning electron microscopy-energy-dispersive x-ray spectroscopy (SEM-EDX) supports the results of modeling: edge sites highly prefer Cs ions and also Ca and Na ions but not K ions.
The effect of mis-specification on mean and selection between the Weibull and lognormal models
Jia, Xiang; Nadarajah, Saralees; Guo, Bo
2018-02-01
The lognormal and Weibull models are commonly used to analyse data. Although selection procedures have been extensively studied, it is possible that the lognormal model could be selected when the true model is Weibull or vice versa. As the mean is important in applications, we focus on the effect of mis-specification on mean. The effect on lognormal mean is first considered if the lognormal sample is wrongly fitted by a Weibull model. The maximum likelihood estimate (MLE) and quasi-MLE (QMLE) of lognormal mean are obtained based on lognormal and Weibull models. Then, the impact is evaluated by computing ratio of biases and ratio of mean squared errors (MSEs) between MLE and QMLE. For completeness, the theoretical results are demonstrated by simulation studies. Next, the effect of the reverse mis-specification on Weibull mean is discussed. It is found that the ratio of biases and the ratio of MSEs are independent of the location and scale parameters of the lognormal and Weibull models. The influence could be ignored if some special conditions hold. Finally, a model selection method is proposed by comparing ratios concerning biases and MSEs. We also present a published data to illustrate the study in this paper.
The Sim-SEQ Project: Comparison of Selected Flow Models for the S-3 Site
Energy Technology Data Exchange (ETDEWEB)
Mukhopadhyay, Sumit; Doughty, Christine A.; Bacon, Diana H.; Li, Jun; Wei, Lingli; Yamamoto, Hajime; Gasda, Sarah E.; Hosseini, Seyyed; Nicot, Jean-Philippe; Birkholzer, Jens
2015-05-23
Sim-SEQ is an international initiative on model comparison for geologic carbon sequestration, with an objective to understand and, if possible, quantify model uncertainties. Model comparison efforts in Sim-SEQ are at present focusing on one specific field test site, hereafter referred to as the Sim-SEQ Study site (or S-3 site). Within Sim-SEQ, different modeling teams are developing conceptual models of CO2 injection at the S-3 site. In this paper, we select five flow models of the S-3 site and provide a qualitative comparison of their attributes and predictions. These models are based on five different simulators or modeling approaches: TOUGH2/EOS7C, STOMP-CO2e, MoReS, TOUGH2-MP/ECO2N, and VESA. In addition to model-to-model comparison, we perform a limited model-to-data comparison, and illustrate how model choices impact model predictions. We conclude the paper by making recommendations for model refinement that are likely to result in less uncertainty in model predictions.
Zeng, Xueqiang; Luo, Gang
2017-12-01
Machine learning is broadly used for clinical data analysis. Before training a model, a machine learning algorithm must be selected. Also, the values of one or more model parameters termed hyper-parameters must be set. Selecting algorithms and hyper-parameter values requires advanced machine learning knowledge and many labor-intensive manual iterations. To lower the bar to machine learning, miscellaneous automatic selection methods for algorithms and/or hyper-parameter values have been proposed. Existing automatic selection methods are inefficient on large data sets. This poses a challenge for using machine learning in the clinical big data era. To address the challenge, this paper presents progressive sampling-based Bayesian optimization, an efficient and automatic selection method for both algorithms and hyper-parameter values. We report an implementation of the method. We show that compared to a state of the art automatic selection method, our method can significantly reduce search time, classification error rate, and standard deviation of error rate due to randomization. This is major progress towards enabling fast turnaround in identifying high-quality solutions required by many machine learning-based clinical data analysis tasks.
Using maximum entropy modeling for optimal selection of sampling sites for monitoring networks
Stohlgren, Thomas J.; Kumar, Sunil; Barnett, David T.; Evangelista, Paul H.
2011-01-01
Environmental monitoring programs must efficiently describe state shifts. We propose using maximum entropy modeling to select dissimilar sampling sites to capture environmental variability at low cost, and demonstrate a specific application: sample site selection for the Central Plains domain (453,490 km2) of the National Ecological Observatory Network (NEON). We relied on four environmental factors: mean annual temperature and precipitation, elevation, and vegetation type. A “sample site” was defined as a 20 km × 20 km area (equal to NEON’s airborne observation platform [AOP] footprint), within which each 1 km2 cell was evaluated for each environmental factor. After each model run, the most environmentally dissimilar site was selected from all potential sample sites. The iterative selection of eight sites captured approximately 80% of the environmental envelope of the domain, an improvement over stratified random sampling and simple random designs for sample site selection. This approach can be widely used for cost-efficient selection of survey and monitoring sites.
Directory of Open Access Journals (Sweden)
Kadhim Raheem Erzaij
2016-06-01
Full Text Available Engineering equipment is essential part in the construction project and usually manufactured with long lead times, large costs and special engineering requirements. Construction manager targets that equipment to be delivered in the site need date with the right quantity, appropriate cost and required quality, and this entails an efficient supplier can satisfy these targets. Selection of engineering equipment supplier is a crucial managerial process .it requires evaluation of multiple suppliers according to multiple criteria. This process is usually performed manually and based on just limited evaluation criteria, so better alternatives may be neglected. Three stages of survey comprised number of public and private companies in Iraqi construction sector were employed to identify main criteria and sub criteria for supplier selection and their priorities.The main criteria identified were quality of product, commercial aspect, delivery, reputation and position, and system quality . An effective technique in multiple criteria decision making (MCDM as analytical hierarchy process (AHP have been used to get importance weights of criteria based on experts judgment. Thereafter, a management software system for Evaluation and Selection of Engineering Equipment Suppliers (ESEES has been developed based on the results obtained from AHP. This model was validated in a case study at municipality of Baghdad involved actual cases of selection pumps suppliers for infrastructure projects .According to experts, this model can improve the current process followed in the supplier selection and aid decision makers to adopt better choices in the domain of selection engineering equipment suppliers.
Model selection for convolutive ICA with an application to spatiotemporal analysis of EEG
DEFF Research Database (Denmark)
Dyrholm, Mads; Makeig, S.; Hansen, Lars Kai
2007-01-01
We present a new algorithm for maximum likelihood convolutive independent component analysis (ICA) in which components are unmixed using stable autoregressive filters determined implicitly by estimating a convolutive model of the mixing process. By introducing a convolutive mixing model...... for the components, we show how the order of the filters in the model can be correctly detected using Bayesian model selection. We demonstrate a framework for deconvolving a subspace of independent components in electroencephalography (EEG). Initial results suggest that in some cases, convolutive mixing may...
Joint Bayesian variable and graph selection for regression models with network-structured predictors
Peterson, C. B.; Stingo, F. C.; Vannucci, M.
2015-01-01
In this work, we develop a Bayesian approach to perform selection of predictors that are linked within a network. We achieve this by combining a sparse regression model relating the predictors to a response variable with a graphical model describing conditional dependencies among the predictors. The proposed method is well-suited for genomic applications since it allows the identification of pathways of functionally related genes or proteins which impact an outcome of interest. In contrast to previous approaches for network-guided variable selection, we infer the network among predictors using a Gaussian graphical model and do not assume that network information is available a priori. We demonstrate that our method outperforms existing methods in identifying network-structured predictors in simulation settings, and illustrate our proposed model with an application to inference of proteins relevant to glioblastoma survival. PMID:26514925
Estimation and Model Selection for Finite Mixtures of Latent Interaction Models
Hsu, Jui-Chen
2011-01-01
Latent interaction models and mixture models have received considerable attention in social science research recently, but little is known about how to handle if unobserved population heterogeneity exists in the endogenous latent variables of the nonlinear structural equation models. The current study estimates a mixture of latent interaction…
Zhang, Guanyang; Basharat, Usmaan; Matzke, Nicholas; Franz, Nico M
2017-04-01
Statistical historical biogeographic methods rely on models that represent various biogeographic processes. Until recently model selection in this domain was not widely used, and the impact of differential model selection on inferring biogeographic scenarios was not well understood. Focusing on Neotropical weevils in the Exophthalmus genus complex (EGC) (Insecta: Curculionidae: Entiminae), we compare three commonly used biogeographic models - DIVA (Dispersal-Vicariance Analysis), DEC (Dispersal-Extinction-Cladogenesis) and BayArea (Bayesian Analysis of Biogeography), and examine the impact of modeling founder-event jump dispersal on historical biogeographic reconstructions. We also investigate the biogeographic events that have shaped patterns of distribution, diversification, and endemism in this weevil lineage. We sample representatives of 65 species of the EGC and 26 outgroup terminals from the Neotropics, including Caribbean islands and the mainland. We reconstruct a molecular phylogeny based on six genes and apply molecular dating using a relaxed clock with three fossil calibration points. Historical biogeographic estimations and alternative biogeographic models are computed and compared with the R package BioGeoBEARS. Model selection strongly favors biogeographic models that include founder-event jump dispersal. Without modeling jump dispersal, estimations based on the three biogeographic models are dramatically different, especially for early-diverging nodes. When jump dispersal is included, the three biogeographic models perform similarly. Accordingly, we show that the Neotropical mainland was colonized by Caribbean species in the early Miocene, and that in situ diversification accounts for a majority (∼75%) of the biogeographic events in the EGC. Our study highlights the need to assess wide-ranging historical biogeographic processes - including founder-event jump dispersal - for best-fitting statistical Caribbean biogeographic reconstructions. Moreover
PRESEMO - a predictive model of codend selectivity - a tool for fishery managers
DEFF Research Database (Denmark)
O'Neill, F.G.; Herrmann, Bent
2007-01-01
parameters are expressed in terms of the gear design parameters and in terms of both catch size and gear design parameters. The potential use of these results in a management context and for the development of more selective gears is highlighted by plotting iso-/(50) and iso-sr curves used to identify gear...... design parameters that give equal estimates of the 50% retention length and the selection range, respectively. It is emphasized that this approach can be extended to consider the influence of other design parameters and, if sufficient relevant quantitative information exists, biological and behavioural...... parameters. As such, the model presented here will provide a better understanding of the selection process, permit a more targeted approach to codend selectivity experiments, and assist fishery managers to assess the impact of proposed technical measures that are introduced to reduce the catch of undersized...
THE MATHEMATICAL MODEL OF SELECTING THE ROLLING STOCK FOR PASSENGER BUS TRANSPORTATION
Directory of Open Access Journals (Sweden)
M. Kostikova
2017-06-01
Full Text Available The problem of choosing buses of different carrying capacity while simultaneously using them on regular urban routes is considered. The indicators that affect the selection of the rolling stock are analyzed. Methods for constructing models, which allow calculating the number of buses of different classes for one city route are proposed. The implementation of the constructed models will make it possible to obtain the savings in capital expenditures, as well as reduce the operational costs of an enterprise.
DEFF Research Database (Denmark)
Kock, Anders Bredahl
2016-01-01
as if only these had been included in the model from the outset. In particular, this implies that it is able to discriminate between stationary and nonstationary autoregressions and it thereby constitutes an addition to the set of unit root tests. Next, and important in practice, we show that choosing...... to perform conservative model selection it has power even against shrinking alternatives of this form and compare it to the plain Lasso....
An integer programming model to limit hospital selection in studies with repeated sampling.
Shwartz, M; Klimberg, R K; Karp, M; Iezzoni, L I; Ash, A S; Heineke, J; Payne, S M; Restuccia, J D
1995-01-01
OBJECTIVE: We describe an integer programming model that, for studies requiring repeated sampling from hospitals, can aid in selecting a limited set of hospitals from which medical records are reviewed. STUDY SETTING: The model is illustrated in the context of two studies: (1) an analysis of the relationship between variations in hospital admission rates across geographic areas and rates of inappropriate admissions; and (2) a validation of computerized algorithms that screen for complications...