Journal articles: 'Multi-model inference'

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Millington, James D. A., and George L. W. Perry. "Multi-Model Inference in Biogeography." Geography Compass 5, no. 7 (July 2011): 448–63. http://dx.doi.org/10.1111/j.1749-8198.2011.00433.x.

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WANG, Hui-Zhen, and Jing-Bo ZHU. "Optimizations of Multi-Aspect Rating Inference Model." Journal of Software 24, no. 7 (January 16, 2014): 1545–56. http://dx.doi.org/10.3724/sp.j.1001.2013.04278.

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Theorell, Axel, and Katharina Nöh. "Reversible jump MCMC for multi-model inference in Metabolic Flux Analysis." Bioinformatics 36, no. 1 (June 19, 2019): 232–40. http://dx.doi.org/10.1093/bioinformatics/btz500.

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Abstract Motivation The validity of model based inference, as used in systems biology, depends on the underlying model formulation. Often, a vast number of competing models is available, that are built on different assumptions, all consistent with the existing knowledge about the studied biological phenomenon. As a remedy for this, Bayesian Model Averaging (BMA) facilitates parameter and structural inferences based on multiple models simultaneously. However, in fields where a vast number of alternative, high-dimensional and non-linear models are involved, the BMA-based inference task is computationally very challenging. Results Here we use BMA in the complex setting of Metabolic Flux Analysis (MFA) to infer whether potentially reversible reactions proceed uni- or bidirectionally, using 13C labeling data and metabolic networks. BMA is applied on a large set of candidate models with differing directionality settings, using a tailored multi-model Markov Chain Monte Carlo (MCMC) approach. The applicability of our algorithm is shown by inferring the in vivo probability of reaction bidirectionalities in a realistic network setup, thereby extending the scope of 13C MFA from parameter to structural inference. Supplementary information Supplementary data are available at Bioinformatics online.

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Mao, W., and J. Gratch. "Modeling Social Causality and Responsibility Judgment in Multi-Agent Interactions." Journal of Artificial Intelligence Research 44 (May 30, 2012): 223–73. http://dx.doi.org/10.1613/jair.3526.

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Social causality is the inference an entity makes about the social behavior of other entities and self. Besides physical cause and effect, social causality involves reasoning about epistemic states of agents and coercive circumstances. Based on such inference, responsibility judgment is the process whereby one singles out individuals to assign responsibility, credit or blame for multi-agent activities. Social causality and responsibility judgment are a key aspect of social intelligence, and a model for them facilitates the design and development of a variety of multi-agent interactive systems. Based on psychological attribution theory, this paper presents a domain-independent computational model to automate social inference and judgment process according to an agents causal knowledge and observations of interaction. We conduct experimental studies to empirically validate the computational model. The experimental results show that our model predicts human judgments of social attributions and makes inferences consistent with what most people do in their judgments. Therefore, the proposed model can be generically incorporated into an intelligent system to augment its social and cognitive functionality.

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Katsanevakis, Stelios. "Modelling fish growth: Model selection, multi-model inference and model selection uncertainty." Fisheries Research 81, no. 2-3 (November 2006): 229–35. http://dx.doi.org/10.1016/j.fishres.2006.07.002.

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Lee, Bong-Keun, Jae-Du Chung, and Keun-Ho Ryu. "Multi-Agent Reinforcement Learning Model based on Fuzzy Inference." Journal of the Korea Contents Association 9, no. 10 (October 28, 2009): 51–58. http://dx.doi.org/10.5392/jkca.2009.9.10.051.

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Yao, Yuan, Hanghang Tong, Xifeng Yan, Feng Xu, and Jian Lu. "Multi-Aspect + Transitivity + Bias: An Integral Trust Inference Model." IEEE Transactions on Knowledge and Data Engineering 26, no. 7 (July 2014): 1706–19. http://dx.doi.org/10.1109/tkde.2013.147.

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Stumpf, Michael P. H., and Thomas Thorne. "Multi-model inference of network properties from incomplete data." Journal of Integrative Bioinformatics 3, no. 2 (December 1, 2006): 123–36. http://dx.doi.org/10.1515/jib-2006-32.

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Summary It has previously been shown that subnets differ from global networks from which they are sampled for all but a very limited number of theoretical network models. These differences are of qualitative as well as quantitative nature, and the properties of subnets may be very different from the corresponding properties in the true, unobserved network. Here we propose a novel approach which allows us to infer aspects of the true network from incomplete network data in a multi-model inference framework. We develop the basic theoretical framework, including procedures for assessing confidence intervals of our estimates and evaluate the performance of this approach in simulation studies and against subnets drawn from the presently available PIN network data in Saccaromyces cerevisiae. We then illustrate the potential power of this new approach by estimating the number of interactions that will be detectable with present experimental approaches in sfour eukaryotic species, inlcuding humans. Encouragingly, where independent datasets are available we obtain consistent estimates from different partial protein interaction networks. We conclude with a discussion of the scope of this approaches and areas for further research

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Mu, He-Qing, Han-Teng Liu, and Ji-Hui Shen. "Copula-Based Uncertainty Quantification (Copula-UQ) for Multi-Sensor Data in Structural Health Monitoring." Sensors 20, no. 19 (October 6, 2020): 5692. http://dx.doi.org/10.3390/s20195692.

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The problem of uncertainty quantification (UQ) for multi-sensor data is one of the main concerns in structural health monitoring (SHM). One important task is multivariate joint probability density function (PDF) modelling. Copula-based statistical inference has attracted significant attention due to the fact that it decouples inferences on the univariate marginal PDF of each random variable and the statistical dependence structure (called copula) among the random variables. This paper proposes the Copula-UQ, composing multivariate joint PDF modelling, inference on model class selection and parameter identification, and probabilistic prediction using incomplete information, for multi-sensor data measured from a SHM system. Multivariate joint PDF is modeled based on the univariate marginal PDFs and the copula. Inference is made by combing the idea of the inference functions for margins and the maximum likelihood estimate. Prediction on the PDF of the target variable, using the complete (from normal sensors) or incomplete information (due to missing data caused by sensor fault issue) of the predictor variable, are made based on the multivariate joint PDF. One example using simulated data and one example using temperature data of a multi-sensor of a monitored bridge are presented to illustrate the capability of the Copula-UQ in joint PDF modelling and target variable prediction.

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Liu, Jingyu, Qiong Wang, Dunbo Zhang, and Li Shen. "Super-Resolution Model Quantized in Multi-Precision." Electronics 10, no. 17 (September 6, 2021): 2176. http://dx.doi.org/10.3390/electronics10172176.

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Deep learning has achieved outstanding results in various tasks in machine learning under the background of rapid increase in equipment’s computing capacity. However, while achieving higher performance and effects, model size is larger, training and inference time longer, the memory and storage occupancy increasing, the computing efficiency shrinking, and the energy consumption augmenting. Consequently, it’s difficult to let these models run on edge devices such as micro and mobile devices. Model compression technology is gradually emerging and researched, for instance, model quantization. Quantization aware training can take more accuracy loss resulting from data mapping in model training into account, which clamps and approximates the data when updating parameters, and introduces quantization errors into the model loss function. In quantization, we found that some stages of the two super-resolution model networks, SRGAN and ESRGAN, showed sensitivity to quantization, which greatly reduced the performance. Therefore, we use higher-bits integer quantization for the sensitive stage, and train the model together in quantization aware training. Although model size was sacrificed a little, the accuracy approaching the original model was achieved. The ESRGAN model was still reduced by nearly 67.14% and SRGAN model was reduced by nearly 68.48%, and the inference time was reduced by nearly 30.48% and 39.85% respectively. What’s more, the PI values of SRGAN and ESRGAN are 2.1049 and 2.2075 respectively.

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Van den Broek, B., W. Wiegerinck, and B. Kappen. "Graphical Model Inference in Optimal Control of Stochastic Multi-Agent Systems." Journal of Artificial Intelligence Research 32 (May 16, 2008): 95–122. http://dx.doi.org/10.1613/jair.2473.

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In this article we consider the issue of optimal control in collaborative multi-agent systems with stochastic dynamics. The agents have a joint task in which they have to reach a number of target states. The dynamics of the agents contains additive control and additive noise, and the autonomous part factorizes over the agents. Full observation of the global state is assumed. The goal is to minimize the accumulated joint cost, which consists of integrated instantaneous costs and a joint end cost. The joint end cost expresses the joint task of the agents. The instantaneous costs are quadratic in the control and factorize over the agents. The optimal control is given as a weighted linear combination of single-agent to single-target controls. The single-agent to single-target controls are expressed in terms of diffusion processes. These controls, when not closed form expressions, are formulated in terms of path integrals, which are calculated approximately by Metropolis-Hastings sampling. The weights in the control are interpreted as marginals of a joint distribution over agent to target assignments. The structure of the latter is represented by a graphical model, and the marginals are obtained by graphical model inference. Exact inference of the graphical model will break down in large systems, and so approximate inference methods are needed. We use naive mean field approximation and belief propagation to approximate the optimal control in systems with linear dynamics. We compare the approximate inference methods with the exact solution, and we show that they can accurately compute the optimal control. Finally, we demonstrate the control method in multi-agent systems with nonlinear dynamics consisting of up to 80 agents that have to reach an equal number of target states.

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Zheng, Lei, Fanchao Qi, Zhiyuan Liu, Yasheng Wang, Qun Liu, and Maosong Sun. "Multi-Channel Reverse Dictionary Model." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 01 (April 3, 2020): 312–19. http://dx.doi.org/10.1609/aaai.v34i01.5365.

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A reverse dictionary takes the description of a target word as input and outputs the target word together with other words that match the description. Existing reverse dictionary methods cannot deal with highly variable input queries and low-frequency target words successfully. Inspired by the description-to-word inference process of humans, we propose the multi-channel reverse dictionary model, which can mitigate the two problems simultaneously. Our model comprises a sentence encoder and multiple predictors. The predictors are expected to identify different characteristics of the target word from the input query. We evaluate our model on English and Chinese datasets including both dictionary definitions and human-written descriptions. Experimental results show that our model achieves the state-of-the-art performance, and even outperforms the most popular commercial reverse dictionary system on the human-written description dataset. We also conduct quantitative analyses and a case study to demonstrate the effectiveness and robustness of our model. All the code and data of this work can be obtained on https://github.com/thunlp/MultiRD.

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Yang, Jingmei, Feng Liu, Boyu Wang, Chaoyang Chen, Timothy Church, Lee Dukes, and Jeffrey O. Smith. "Blood Pressure States Transition Inference Based on Multi-State Markov Model." IEEE Journal of Biomedical and Health Informatics 25, no. 1 (January 2021): 237–46. http://dx.doi.org/10.1109/jbhi.2020.3006217.

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Ren, Ming, Chi Cheung, and Gao Xiao. "Gaussian Process Based Bayesian Inference System for Intelligent Surface Measurement." Sensors 18, no. 11 (November 21, 2018): 4069. http://dx.doi.org/10.3390/s18114069.

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This paper presents a Gaussian process based Bayesian inference system for the realization of intelligent surface measurement on multi-sensor instruments. The system considers the surface measurement as a time series data collection process, and the Gaussian process is used as mathematical foundation to establish an inferring plausible model to aid the measurement process via multi-feature classification and multi-dataset regression. Multi-feature classification extracts and classifies the geometric features of the measured surfaces at different scales to design an appropriate composite covariance kernel and corresponding initial sampling strategy. Multi-dataset regression takes the designed covariance kernel as input to fuse the multi-sensor measured datasets with Gaussian process model, which is further used to adaptively refine the initial sampling strategy by taking the credibility of the fused model as the critical sampling criteria. Hence, intelligent sampling can be realized with consecutive learning process with full Bayesian treatment. The statistical nature of the Gaussian process model combined with various powerful covariance kernel functions offer the system great flexibility for different kinds of complex surfaces.

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Chiang, Sharon, Michele Guindani, Hsiang J. Yeh, Zulfi Haneef, John M. Stern, and Marina Vannucci. "Bayesian vector autoregressive model for multi-subject effective connectivity inference using multi-modal neuroimaging data." Human Brain Mapping 38, no. 3 (November 16, 2016): 1311–32. http://dx.doi.org/10.1002/hbm.23456.

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Jamali, A., H. Babaei, N. Nariman-Zadeh, SH Ashraf Talesh, and T. Mirzababaie Mostofi. "Multi-objective optimum design of ANFIS for modelling and prediction of deformation of thin plates subjected to hydrodynamic impact loading." Proceedings of the Institution of Mechanical Engineers, Part L: Journal of Materials: Design and Applications 234, no. 3 (July 22, 2016): 368–78. http://dx.doi.org/10.1177/1464420716660332.

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Drop hammer impact experiments have been carried out to assess the dynamic plastic response of fully clamped circular and rectangular plates made of aluminum and steel subjected to hydrodynamic impact loading at various energy levels. Also, the effective parameters in forming process are proposed in non-dimensional forms for modeling and prediction of the central deflection of plates using adaptive neuro-fuzzy inference system in conjunction with genetic algorithm and singular value decomposition method. Genetic algorithm is used for optimal scheme of Gaussian membership function’s variables and multi-objective Pareto optimal design of adaptive neuro-fuzzy inference system model. Also, the singular value decomposition method is applied to compute the linear parameters of the adaptive neuro-fuzzy inference system method. The important conflicting objectives of developed adaptive neuro-fuzzy inference system, namely, training error and prediction error, are obtained by dividing date sets into two parts. Hence, various optimal choices of adaptive neuro-fuzzy inference system model are provided which are non-dominated states from each other. Moreover, optimal Pareto front of such model leads to trade-off between the conflicting pair of considered objectives for two series of experiments. The results of this work indicate that multi-objective Pareto optimal design of adaptive neuro-fuzzy inference system predicts central deflection of plates with a good accuracy. In addition, the comparison between the adaptive neuro-fuzzy inference system model and exiting one demonstrates superior performance of the present approach in simulating central deflection of plates.

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Burnham, Kenneth P., and David R. Anderson. "Kullback-Leibler information as a basis for strong inference in ecological studies." Wildlife Research 28, no. 2 (2001): 111. http://dx.doi.org/10.1071/wr99107.

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We describe an information-theoretic paradigm for analysis of ecological data, based on Kullback–Leibler information, that is an extension of likelihood theory and avoids the pitfalls of null hypothesis testing. Information-theoretic approaches emphasise a deliberate focus on the a priori science in developing a set of multiple working hypotheses or models. Simple methods then allow these hypotheses (models) to be ranked from best to worst and scaled to reflect a strength of evidence using the likelihood of each model (gi), given the data and the models in the set (i.e. L(gi | data)). In addition, a variance component due to model-selection uncertainty is included in estimates of precision. There are many cases where formal inference can be based on all the models in the a priori set and this multi-model inference represents a powerful, new approach to valid inference. Finally, we strongly recommend inferences based on a priori considerations be carefully separated from those resulting from some form of data dredging. An example is given for questions related to age- and sex-dependent rates of tag loss in elephant seals (Mirounga leonina).

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Contreras, Andres A., Olivier P. Le Maître, Wilkins Aquino, and Omar M. Knio. "Multi-model polynomial chaos surrogate dictionary for Bayesian inference in elasticity problems." Probabilistic Engineering Mechanics 46 (October 2016): 107–19. http://dx.doi.org/10.1016/j.probengmech.2016.08.004.

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Carvajal-Rodríguez, A. "Multi-model inference of non-random mating from an information theoretic approach." Theoretical Population Biology 131 (February 2020): 38–53. http://dx.doi.org/10.1016/j.tpb.2019.11.002.

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Kato, Masashi, Qian Ji Gao, Hiroshi Chigira, Hiroyuki Shindo, and Masato Inoue. "A haplotype inference method based on sparsely connected multi-body ising model." Journal of Physics: Conference Series 233 (June 1, 2010): 012022. http://dx.doi.org/10.1088/1742-6596/233/1/012022.

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Ianelli, James, Kirstin K. Holsman, André E. Punt, and Kerim Aydin. "Multi-model inference for incorporating trophic and climate uncertainty into stock assessments." Deep Sea Research Part II: Topical Studies in Oceanography 134 (December 2016): 379–89. http://dx.doi.org/10.1016/j.dsr2.2015.04.002.

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Xiong, Juan, Qiyu Fang, Jialing Chen, Yingxin Li, Huiyi Li, Wenjie Li, and Xujuan Zheng. "States Transitions Inference of Postpartum Depression Based on Multi-State Markov Model." International Journal of Environmental Research and Public Health 18, no. 14 (July 13, 2021): 7449. http://dx.doi.org/10.3390/ijerph18147449.

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Background: Postpartum depression (PPD) has been recognized as a severe public health problem worldwide due to its high incidence and the detrimental consequences not only for the mother but for the infant and the family. However, the pattern of natural transition trajectories of PPD has rarely been explored. Methods: In this research, a quantitative longitudinal study was conducted to explore the PPD progression process, providing information on the transition probability, hazard ratio, and the mean sojourn time in the three postnatal mental states, namely normal state, mild PPD, and severe PPD. The multi-state Markov model was built based on 912 depression status assessments in 304 Chinese primiparous women over multiple time points of six weeks postpartum, three months postpartum, and six months postpartum. Results: Among the 608 PPD status transitions from one visit to the next visit, 6.2% (38/608) showed deterioration of mental status from the level at the previous visit; while 40.0% (243/608) showed improvement at the next visit. A subject in normal state who does transition then has a probability of 49.8% of worsening to mild PPD, and 50.2% to severe PPD. A subject with mild PPD who does transition has a 20.0% chance of worsening to severe PPD. A subject with severe PPD is more likely to improve to mild PPD than developing to the normal state. On average, the sojourn time in the normal state, mild PPD, and severe PPD was 64.12, 6.29, and 9.37 weeks, respectively. Women in normal state had 6.0%, 8.5%, 8.7%, and 8.8% chances of progress to severe PPD within three months, nine months, one year, and three years, respectively. Increased all kinds of supports were associated with decreased risk of deterioration from normal state to severe PPD (hazard ratio, HR: 0.42–0.65); and increased informational supports, evaluation of support, and maternal age were associated with alleviation from severe PPD to normal state (HR: 1.46–2.27). Conclusions: The PPD state transition probabilities caused more attention and awareness about the regular PPD screening for postnatal women and the timely intervention for women with mild or severe PPD. The preventive actions on PPD should be conducted at the early stages, and three yearly; at least one yearly screening is strongly recommended. Emotional support, material support, informational support, and evaluation of support had significant positive associations with the prevention of PPD progression transitions. The derived transition probabilities and sojourn time can serve as an importance reference for health professionals to make proactive plans and target interventions for PPD.

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Zhu, Jianxiao, Xu Li, Peng Jin, Qimin Xu, Zhengliang Sun, and Xiang Song. "MME-YOLO: Multi-Sensor Multi-Level Enhanced YOLO for Robust Vehicle Detection in Traffic Surveillance." Sensors 21, no. 1 (December 23, 2020): 27. http://dx.doi.org/10.3390/s21010027.

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As an effective means of solving collision problems caused by the limited perspective on board, the cooperative roadside system is gaining popularity. To improve the vehicle detection abilities in such online safety systems, in this paper, we propose a novel multi-sensor multi-level enhanced convolutional network model, called multi-sensor multi-level enhanced convolutional network architecture (MME-YOLO), with consideration of hybrid realistic scene of scales, illumination, and occlusion. MME-YOLO consists of two tightly coupled structures, i.e., the enhanced inference head and the LiDAR-Image composite module. More specifically, the enhanced inference head preliminarily equips the network with stronger inference abilities for redundant visual cues by attention-guided feature selection blocks and anchor-based/anchor-free ensemble head. Furthermore, the LiDAR-Image composite module cascades the multi-level feature maps from the LiDAR subnet to the image subnet, which strengthens the generalization of the detector in complex scenarios. Compared with YOLOv3, the enhanced inference head achieves a 5.83% and 4.88% mAP improvement on visual dataset LVSH and UA-DETRAC, respectively. Integrated with the composite module, the overall architecture gains 91.63% mAP in the collected Road-side Dataset. Experiments show that even under the abnormal lightings and the inconsistent scales at evening rush hours, the proposed MME-YOLO maintains reliable recognition accuracy and robust detection performance.

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Shum, Michael, Max Kleiman-Weiner, Michael L. Littman, and Joshua B. Tenenbaum. "Theory of Minds: Understanding Behavior in Groups through Inverse Planning." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 6163–70. http://dx.doi.org/10.1609/aaai.v33i01.33016163.

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Human social behavior is structured by relationships. We form teams, groups, tribes, and alliances at all scales of human life. These structures guide multi-agent cooperation and competition, but when we observe others these underlying relationships are typically unobservable and hence must be inferred. Humans make these inferences intuitively and flexibly, often making rapid generalizations about the latent relationships that underlie behavior from just sparse and noisy observations. Rapid and accurate inferences are important for determining who to cooperate with, who to compete with, and how to cooperate in order to compete. Towards the goal of building machine-learning algorithms with human-like social intelligence, we develop a generative model of multiagent action understanding based on a novel representation for these latent relationships called Composable Team Hierarchies (CTH). This representation is grounded in the formalism of stochastic games and multi-agent reinforcement learning. We use CTH as a target for Bayesian inference yielding a new algorithm for understanding behavior in groups that can both infer hidden relationships as well as predict future actions for multiple agents interacting together. Our algorithm rapidly recovers an underlying causal model of how agents relate in spatial stochastic games from just a few observations. The patterns of inference made by this algorithm closely correspond with human judgments and the algorithm makes the same rapid generalizations that people do.

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Zhang, Jianming, Chaoquan Lu, Jin Wang, Xiao-Guang Yue, Se-Jung Lim, Zafer Al-Makhadmeh, and Amr Tolba. "Training Convolutional Neural Networks with Multi-Size Images and Triplet Loss for Remote Sensing Scene Classification." Sensors 20, no. 4 (February 21, 2020): 1188. http://dx.doi.org/10.3390/s20041188.

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Many remote sensing scene classification algorithms improve their classification accuracy by additional modules, which increases the parameters and computing overhead of the model at the inference stage. In this paper, we explore how to improve the classification accuracy of the model without adding modules at the inference stage. First, we propose a network training strategy of training with multi-size images. Then, we introduce more supervision information by triplet loss and design a branch for the triplet loss. In addition, dropout is introduced between the feature extractor and the classifier to avoid over-fitting. These modules only work at the training stage and will not bring about the increase in model parameters at the inference stage. We use Resnet18 as the baseline and add the three modules to the baseline. We perform experiments on three datasets: AID, NWPU-RESISC45, and OPTIMAL. Experimental results show that our model combined with the three modules is more competitive than many existing classification algorithms. In addition, ablation experiments on OPTIMAL show that dropout, triplet loss, and training with multi-size images improve the overall accuracy of the model on the test set by 0.53%, 0.38%, and 0.7%, respectively. The combination of the three modules improves the overall accuracy of the model by 1.61%. It can be seen that the three modules can improve the classification accuracy of the model without increasing model parameters at the inference stage, and training with multi-size images brings a greater gain in accuracy than the other two modules, but the combination of the three modules will be better.

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Frermann, Lea, Shay B. Cohen, and Mirella Lapata. "Whodunnit? Crime Drama as a Case for Natural Language Understanding." Transactions of the Association for Computational Linguistics 6 (December 2018): 1–15. http://dx.doi.org/10.1162/tacl_a_00001.

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In this paper we argue that crime drama exemplified in television programs such as CSI: Crime Scene Investigation is an ideal testbed for approximating real-world natural language understanding and the complex inferences associated with it. We propose to treat crime drama as a new inference task, capitalizing on the fact that each episode poses the same basic question (i.e., who committed the crime) and naturally provides the answer when the perpetrator is revealed. We develop a new dataset based on CSI episodes, formalize perpetrator identification as a sequence labeling problem, and develop an LSTM-based model which learns from multi-modal data. Experimental results show that an incremental inference strategy is key to making accurate guesses as well as learning from representations fusing textual, visual, and acoustic input.

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Li-jia, LIU, HU Jian-wang, and SUN Hui-xian. "Fault Diagnosis Reasoning Algorithm Based on Multi-signal Model." MATEC Web of Conferences 173 (2018): 03022. http://dx.doi.org/10.1051/matecconf/201817303022.

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The multi-signal model is modeled in the fault space and combines the structural model and the dependent model of the system. The modeling work is easy to implement in different layers and is very suitable for fault modeling and fault diagnosis of the Command and control system. After the model is established, how to meet the requirements of fault coverage and fault isolation rate, fault diagnosis reasoning algorithm is particularly important. The system dependency matrix is obtained by establishing a multi-signal model. Based on this, the fault diagnosis reasoning algorithm is studied. The fault diagnosis of Apollo spacecraft before launch is taken as an example to verify the effectiveness of fault diagnosis inference algorithm based on multi-signal model.

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Harrison, Xavier A., Lynda Donaldson, Maria Eugenia Correa-Cano, Julian Evans, David N. Fisher, Cecily E. D. Goodwin, Beth S. Robinson, David J. Hodgson, and Richard Inger. "A brief introduction to mixed effects modelling and multi-model inference in ecology." PeerJ 6 (May 23, 2018): e4794. http://dx.doi.org/10.7717/peerj.4794.

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The use of linear mixed effects models (LMMs) is increasingly common in the analysis of biological data. Whilst LMMs offer a flexible approach to modelling a broad range of data types, ecological data are often complex and require complex model structures, and the fitting and interpretation of such models is not always straightforward. The ability to achieve robust biological inference requires that practitioners know how and when to apply these tools. Here, we provide a general overview of current methods for the application of LMMs to biological data, and highlight the typical pitfalls that can be encountered in the statistical modelling process. We tackle several issues regarding methods of model selection, with particular reference to the use of information theory and multi-model inference in ecology. We offer practical solutions and direct the reader to key references that provide further technical detail for those seeking a deeper understanding. This overview should serve as a widely accessible code of best practice for applying LMMs to complex biological problems and model structures, and in doing so improve the robustness of conclusions drawn from studies investigating ecological and evolutionary questions.

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Chun Shen, Lei Liu, Shuai Lu, Fan Luo, and Yi Lu. "A Multi-strategy Fuzzy Inference Negotiation Model Based on the Offer Satisfactory Degree." Journal of Convergence Information Technology 7, no. 4 (March 31, 2012): 17–25. http://dx.doi.org/10.4156/jcit.vol7.issue4.3.

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Mann, Richard P., Andrea Perna, Daniel Strömbom, Roman Garnett, James E. Herbert-Read, David J. T. Sumpter, and Ashley J. W. Ward. "Multi-scale Inference of Interaction Rules in Animal Groups Using Bayesian Model Selection." PLoS Computational Biology 8, no. 1 (January 5, 2012): e1002308. http://dx.doi.org/10.1371/journal.pcbi.1002308.

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Mann, Richard P., Andrea Perna, Daniel Strömbom, Roman Garnett, James E. Herbert-Read, David J. T. Sumpter, and Ashley J. W. Ward. "Multi-scale Inference of Interaction Rules in Animal Groups Using Bayesian Model Selection." PLoS Computational Biology 9, no. 3 (March 21, 2013): e1002961. http://dx.doi.org/10.1371/journal.pcbi.1002961.

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Ravichandran M, Subramanian K M, and Jothikumar R. "An Enhanced and Efficient Multi-View Clustering Trust Inference Approach by GA Model." International Journal of Information Technology and Web Engineering 14, no. 4 (October 2019): 64–78. http://dx.doi.org/10.4018/ijitwe.2019100104.

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Multi-view affinity propagation (MAP) methods are widely accepted techniques, measure the within-view clustering and clustering consistency. These suffer from similarity and correlation between clusters. The trust and similarity measured was introduced as a new approach to overcome the problem. But these approaches suffer from low accuracy and coverage due to avoidance of implicit trust. So, a framework called multi-view clustering based on gray affinity (MVC-GA) created by integrating both similarity and implicit trust. Similarity between two clusters is obtained by applying the Pearson Correlation Coefficient-based similarity. It utilizes the collaborative filter-based trust evaluation for each clustered view in terms of the similarity based on the gray affinity nn algorithm. Classification of incomplete occurrences is addressed based on GA Function. Experiments on the benchmark data sets have been performed to validate the proposed framework. It is shown that MVC-GA can improve the multi-view clustering accuracy and coverage.

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Stumpf, Michael P. H. "Multi-model and network inference based on ensemble estimates: avoiding the madness of crowds." Journal of The Royal Society Interface 17, no. 171 (October 2020): 20200419. http://dx.doi.org/10.1098/rsif.2020.0419.

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Recent progress in theoretical systems biology, applied mathematics and computational statistics allows us to compare the performance of different candidate models at describing a particular biological system quantitatively. Model selection has been applied with great success to problems where a small number—typically less than 10—of models are compared, but recent studies have started to consider thousands and even millions of candidate models. Often, however, we are left with sets of models that are compatible with the data, and then we can use ensembles of models to make predictions. These ensembles can have very desirable characteristics, but as I show here are not guaranteed to improve on individual estimators or predictors. I will show in the cases of model selection and network inference when we can trust ensembles, and when we should be cautious. The analyses suggest that the careful construction of an ensemble—choosing good predictors—is of paramount importance, more than had perhaps been realized before: merely adding different methods does not suffice. The success of ensemble network inference methods is also shown to rest on their ability to suppress false-positive results. A Jupyter notebook which allows carrying out an assessment of ensemble estimators is provided.

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Shen, Zhiqiang, Zhankui He, and Xiangyang Xue. "MEAL: Multi-Model Ensemble via Adversarial Learning." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 4886–93. http://dx.doi.org/10.1609/aaai.v33i01.33014886.

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Often the best performing deep neural models are ensembles of multiple base-level networks. Unfortunately, the space required to store these many networks, and the time required to execute them at test-time, prohibits their use in applications where test sets are large (e.g., ImageNet). In this paper, we present a method for compressing large, complex trained ensembles into a single network, where knowledge from a variety of trained deep neural networks (DNNs) is distilled and transferred to a single DNN. In order to distill diverse knowledge from different trained (teacher) models, we propose to use adversarial-based learning strategy where we define a block-wise training loss to guide and optimize the predefined student network to recover the knowledge in teacher models, and to promote the discriminator network to distinguish teacher vs. student features simultaneously. The proposed ensemble method (MEAL) of transferring distilled knowledge with adversarial learning exhibits three important advantages: (1) the student network that learns the distilled knowledge with discriminators is optimized better than the original model; (2) fast inference is realized by a single forward pass, while the performance is even better than traditional ensembles from multi-original models; (3) the student network can learn the distilled knowledge from a teacher model that has arbitrary structures. Extensive experiments on CIFAR-10/100, SVHN and ImageNet datasets demonstrate the effectiveness of our MEAL method. On ImageNet, our ResNet-50 based MEAL achieves top-1/5 21.79%/5.99% val error, which outperforms the original model by 2.06%/1.14%.

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Lin, Cheng-Jian, Chi-Yung Lee, and Cheng-Hung Chen. "A Novel Neuro-Fuzzy Inference System with Multi-Level Membership Function for Classification Applications." Journal of Advanced Computational Intelligence and Intelligent Informatics 11, no. 4 (April 20, 2007): 365–72. http://dx.doi.org/10.20965/jaciii.2007.p0365.

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In this paper, a novel neuro-fuzzy inference system with multi-level membership function (NFIS_MMF) for classification applications is proposed. The NFIS_MMF model is a five-layer structure, which combines the traditional Takagi-Sugeno-Kang (TSK). Layer 2 of the NFIS_MMF model contains multi-level membership functions, which are multilevel activation functions. A self-constructing learning algorithm, which consists of the self-clustering algorithm (SCA), fuzzy entropy, and the backpropagation algorithm, is also proposed to construct the NFIS_MMF model and perform parameter learning. Simulations were conducted to show the performance and applicability of the proposed model.

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Chua, Chew Lian, G. C. Lim, and Penelope Smith. "A BAYESIAN SIMULATION APPROACH TO INFERENCE ON A MULTI-STATE LATENT FACTOR INTENSITY MODEL." Australian & New Zealand Journal of Statistics 53, no. 2 (June 2011): 179–95. http://dx.doi.org/10.1111/j.1467-842x.2011.00625.x.

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Li, Yuhui, Tao Li, and Wan Dong. "Multi-model image retrieval method based on rough set inference and colour mutual information." International Journal of Collaborative Intelligence 1, no. 3 (2016): 205. http://dx.doi.org/10.1504/ijci.2016.077114.

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Morfeld, P., and R. J. McCunney. "Carbon black and lung cancer - testing a novel exposure metric by multi-model inference." Occupational and Environmental Medicine 68, Suppl_1 (September 1, 2011): A114. http://dx.doi.org/10.1136/oemed-2011-100382.379.

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Wasserman, Tzeidle N., Samuel A. Cushman, Michael K. Schwartz, and David O. Wallin. "Spatial scaling and multi-model inference in landscape genetics: Martes americana in northern Idaho." Landscape Ecology 25, no. 10 (August 29, 2010): 1601–12. http://dx.doi.org/10.1007/s10980-010-9525-7.

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Golbon, Reza, Joseph Ochieng Ogutu, Marc Cotter, and Joachim Sauerborn. "Rubber yield prediction by meteorological conditions using mixed models and multi-model inference techniques." International Journal of Biometeorology 59, no. 12 (April 1, 2015): 1747–59. http://dx.doi.org/10.1007/s00484-015-0983-0.

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Moltchanov, Dmitri, Alexander Antonov, Arkady Kluchev, Karolina Borunova, Pavel Kustarev, Vitaly Petrov, Yevgeni Koucheryavy, and Alexey Platunov. "Statistical Traffic Properties and Model Inference for Shared Cache Interface in Multi-Core CPUs." IEEE Access 4 (2016): 4829–39. http://dx.doi.org/10.1109/access.2016.2603169.

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Duan, Youjian, Charles P. Madenjian, Yingming Zhao, and Bin Huo. "Modeling round goby growth in Lake Michigan and Lake Huron with multi-model inference." Fisheries Research 236 (April 2021): 105842. http://dx.doi.org/10.1016/j.fishres.2020.105842.

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Morfeld, Peter, and Robert J. McCunney. "Carbon black and lung cancer-testing a novel exposure metric by multi-model inference." American Journal of Industrial Medicine 52, no. 11 (November 2009): 890–99. http://dx.doi.org/10.1002/ajim.20754.

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Elbaz, Khalid, Shui-Long Shen, Annan Zhou, Da-Jun Yuan, and Ye-Shuang Xu. "Optimization of EPB Shield Performance with Adaptive Neuro-Fuzzy Inference System and Genetic Algorithm." Applied Sciences 9, no. 4 (February 22, 2019): 780. http://dx.doi.org/10.3390/app9040780.

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The prediction of earth pressure balance (EPB) shield performance is an essential part of project scheduling and cost estimation of tunneling projects. This paper establishes an efficient multi-objective optimization model to predict the shield performance during the tunneling process. This model integrates the adaptive neuro-fuzzy inference system (ANFIS) with the genetic algorithm (GA). The hybrid model uses shield operational parameters as inputs and computes the advance rate as output. GA enhances the accuracy of ANFIS for runtime parameters tuning by multi-objective fitness function. Prior to modeling, datasets were established, and critical operating parameters were identified through principal component analysis. Then, the tunneling case for Guangzhou metro line number 9 was adopted to verify the applicability of the proposed model. Results were then compared with those of the ANFIS model. The comparison showed that the multi-objective ANFIS-GA model is more successful than the ANFIS model in predicting the advance rate with a high accuracy, which can be used to guide the tunnel performance in the field.

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Al-Zou’bi, Loai M. A., Amer I. Al-Omari, Ahmad M. Al-Khazalah, and Raed A. Alzghool. "Inference of Adaptive methods for Multi-Stage skew-t Simulated Data." European Scientific Journal, ESJ 13, no. 24 (August 31, 2017): 448. http://dx.doi.org/10.19044/esj.2017.v13n24p448.

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Multilevel models can be used to account for clustering in data from multi-stage surveys. In some cases, the intra-cluster correlation may be close to zero, so that it may seem reasonable to ignore clustering and fit a single level model. This article proposes several adaptive strategies for allowing for clustering in regression analysis of multi-stage survey data. The approach is based on testing whether the cluster-level variance component is zero. If this hypothesis is retained, then variance estimates are calculated ignoring clustering; otherwise, clustering is reflected in variance estimation. A simple simulation study is used to evaluate the various procedures.

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SALTZ, DAVID. "Statistical Inference and Decision Making in Conservation Biology." Israel Journal of Ecology and Evolution 57, no. 4 (May 6, 2010): 309–17. http://dx.doi.org/10.1560/ijee.57.4.309.

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Since the formulation of hypothesis testing by Neyman and Pearson in 1933, the approach has been subject to continuous criticism. Yet, until recently this criticism, for the most part, has gone unheeded. The negative appraisal focuses mainly on the fact thatP-valuesprovide no evidential support for either the null hypothesis (H0) or the alternative hypothesis (Ha). Although hypothesis testing done under tightly controlled conditions can provide some insight regarding the alternative hypothesis based on the uncertainty ofH0, strictly speaking, this does not constitute evidence. More importantly, well controlled research environments rarely exist in field-centered sciences such as ecology. These problems are manifestly more acute in applied field sciences, such as conservation biology, that are expected to support decision making, often under crisis conditions. In conservation biology, the consequences of a Type II error are often far worse than a Type I error. The "advantage" afforded toH0by setting the probability of committing a Type I error (α) to a low value (0.05), in effect, increases the probability of committing a Type II error, which can lead to disastrous practical consequences. In the past decade, multi-model inference using information-theoretic or Bayesian approaches have been offered as better alternatives. These techniques allow comparing a series of models on equal grounds. Using these approaches, it is unnecessary to select a single "best" model. Rather, the parameters needed for decision making can be averaged across all models, weighted according to the support accorded each model. Here, I present a hypothetical example of animal counts that suggest a possible population decline, and analyze the data using hypothesis testing and an information-theoretic approach. A comparison between the two approaches highlights the shortcomings of hypothesis testing and advantages of multi-model inference.

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Zhang, Feng-Yi, and Zhi-Gao Liao. "A New ANFIS Model based on Multi-Input Hamacher T-norm and Subtract Clustering." Open Mechanical Engineering Journal 8, no. 1 (December 31, 2014): 833–38. http://dx.doi.org/10.2174/1874155x01408010833.

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This paper proposed a novel adaptive neuro-fuzzy inference system (ANFIS), which combines subtract clustering, employs adaptive Hamacher T-norm and improves the prediction ability of ANFIS. The expression of multiinput Hamacher T-norm and its relative feather has been originally given, which supports the operation of the proposed system. Empirical study has testified that the proposed model overweighs early work in the aspect of benchmark Box- Jenkins dataset and may provide a practical way to measure the importance of each rule.

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Li, Guangyu, Bo Jiang, Hao Zhu, Zhengping Che, and Yan Liu. "Generative Attention Networks for Multi-Agent Behavioral Modeling." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 05 (April 3, 2020): 7195–202. http://dx.doi.org/10.1609/aaai.v34i05.6209.

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Understanding and modeling behavior of multi-agent systems is a central step for artificial intelligence. Here we present a deep generative model which captures behavior generating process of multi-agent systems, supports accurate predictions and inference, infers how agents interact in a complex system, as well as identifies agent groups and interaction types. Built upon advances in deep generative models and a novel attention mechanism, our model can learn interactions in highly heterogeneous systems with linear complexity in the number of agents. We apply this model to three multi-agent systems in different domains and evaluate performance on a diverse set of tasks including behavior prediction, interaction analysis and system identification. Experimental results demonstrate its ability to model multi-agent systems, yielding improved performance over competitive baselines. We also show the model can successfully identify agent groups and interaction types in these systems. Our model offers new opportunities to predict complex multi-agent behaviors and takes a step forward in understanding interactions in multi-agent systems.

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Yu, Lu, Chuxu Zhang, Shangsong Liang, and Xiangliang Zhang. "Multi-Order Attentive Ranking Model for Sequential Recommendation." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 5709–16. http://dx.doi.org/10.1609/aaai.v33i01.33015709.

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In modern e-commerce, the temporal order behind users’ transactions implies the importance of exploiting the transition dependency among items for better inferring what a user prefers to interact in “near future”. The types of interaction among items are usually divided into individual-level interaction that can stand out the transition order between a pair of items, or union-level relation between a set of items and single one. However, most of existing work only captures one of them from a single view, especially on modeling the individual-level interaction. In this paper, we propose a Multi-order Attentive Ranking Model (MARank) to unify both individual- and union-level item interaction into preference inference model from multiple views. The idea is to represent user’s short-term preference by embedding user himself and a set of present items into multi-order features from intermedia hidden status of a deep neural network. With the help of attention mechanism, we can obtain a unified embedding to keep the individual-level interactions with a linear combination of mapped items’ features. Then, we feed the aggregated embedding to a designed residual neural network to capture union-level interaction. Thorough experiments are conducted to show the features of MARank under various component settings. Furthermore experimental results on several public datasets show that MARank significantly outperforms the state-of-the-art baselines on different evaluation metrics. The source code can be found at https://github.com/voladorlu/MARank.

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Koprivec, Filip, Klemen Kenda, and Beno Šircelj. "FASTENER Feature Selection for Inference from Earth Observation Data." Entropy 22, no. 11 (October 23, 2020): 1198. http://dx.doi.org/10.3390/e22111198.

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In this paper, a novel feature selection algorithm for inference from high-dimensional data (FASTENER) is presented. With its multi-objective approach, the algorithm tries to maximize the accuracy of a machine learning algorithm with as few features as possible. The algorithm exploits entropy-based measures, such as mutual information in the crossover phase of the iterative genetic approach. FASTENER converges to a (near) optimal subset of features faster than other multi-objective wrapper methods, such as POSS, DT-forward and FS-SDS, and achieves better classification accuracy than similarity and information theory-based methods currently utilized in earth observation scenarios. The approach was primarily evaluated using the earth observation data set for land-cover classification from ESA’s Sentinel-2 mission, the digital elevation model and the ground truth data of the Land Parcel Identification System from Slovenia. For land cover classification, the algorithm gives state-of-the-art results. Additionally, FASTENER was tested on open feature selection data sets and compared to the state-of-the-art methods. With fewer model evaluations, the algorithm yields comparable results to DT-forward and is superior to FS-SDS. FASTENER can be used in any supervised machine learning scenario.

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Journal articles on the topic 'Multi-model inference'

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