Joining Forces of Bayesian and Frequentist Methodology: A Study for Inference in the Presence of Non-Identifiability

Increasingly complex applications involve large datasets in combination with non-linear and high dimensional mathematical models. In this context, statistical inference is a challenging issue that calls for pragmatic approaches that take advantage of both Bayesian and frequentist methods. The elegance of Bayesian methodology is founded in the propagation of information content provided by experimental data and prior assumptions to the posterior probability distribution of model predictions. However, for complex applications experimental data and prior assumptions potentially constrain the posterior probability distribution insufficiently. In these situations Bayesian Markov chain Monte Carlo sampling can be infeasible. From a frequentist point of view insufficient experimental data and prior assumptions can be interpreted as non-identifiability. The profile likelihood approach offers to detect and to resolve non-identifiability by experimental design iteratively. Therefore, it allows one to better constrain the posterior probability distribution until Markov chain Monte Carlo sampling can be used securely. Using an application from cell biology we compare both methods and show that a successive application of both methods facilitates a realistic assessment of uncertainty in model predictions.Comment: Article to appear in Phil. Trans. Roy. Soc.

Bayesian Computing with INLA: A Review

The key operation in Bayesian inference is to compute high-dimensional integrals. An old approximate technique is the Laplace method or approximation, which dates back to Pierre-Simon Laplace (1774). This simple idea approximates the integrand with a second-order Taylor expansion around the mode and computes the integral analytically. By developing a nested version of this classical idea, combined with modern numerical techniques for sparse matrices, we obtain the approach of integrated nested Laplace approximations (INLA) to do approximate Bayesian inference for latent Gaussian models (LGMs). LGMs represent an important model abstraction for Bayesian inference and include a large proportion of the statistical models used today. In this review, we discuss the reasons for the success of the INLA approach, the R-INLA package, why it is so accurate, why the approximations are very quick to compute, and why LGMs make such a useful concept for Bayesian computing

Evaluation of different heat transfer conditions on an automotive turbocharger

This paper presents a combination of theoretical and experimental investigations for determining the main heat fluxes within a turbocharger. These investigations consider several engine speeds and loads as well as different methods of conduction, convection, and radiation heat transfer on the turbocharger. A one-dimensional heat transfer model of the turbocharger has been developed in combination with simulation of a turbocharged engine that includes the heat transfer of the turbocharger. Both the heat transfer model and the simulation were validated against experimental measurements. Various methods were compared for calculating heat transfer from the external surfaces of the turbocharger, and one new method was suggested. The effects of different heat transfer conditions were studied on the heat fluxes of the turbocharger using experimental techniques. The different heat transfer conditions on the turbocharger created dissimilar temperature gradients across the turbocharger. The results show that changing the convection heat transfer condition around the turbocharger affects the heat fluxes more noticeably than changing the radiation and conduction heat transfer conditions. Moreover, the internal heat transfers from the turbine to the bearing housing and from the bearing housing to the compressor are significant, but there is an order of magnitude difference between these heat transfer rates.The Swedish Energy Agency and KTH Royal Institute of Technology sponsored this work within the Competence Centre for Gas Exchange (CCGEx).Aghaali, H.; Angström, H.; Serrano Cruz, JR. (2015). Evaluation of different heat transfer conditions on an automotive turbocharger. International Journal of Engine Research. 16(2):137-151. doi:10.1177/1468087414524755S137151162Romagnoli, A., & Martinez-Botas, R. (2012). Heat transfer analysis in a turbocharger turbine: An experimental and computational evaluation. Applied Thermal Engineering, 38, 58-77. doi:10.1016/j.applthermaleng.2011.12.022Romagnoli, A., & Martinez-Botas, R. (2009). Heat Transfer on a Turbocharger Under Constant Load Points. Volume 5: Microturbines and Small Turbomachinery; Oil and Gas Applications. doi:10.1115/gt2009-59618Baines, N., Wygant, K. D., & Dris, A. (2010). The Analysis of Heat Transfer in Automotive Turbochargers. Journal of Engineering for Gas Turbines and Power, 132(4). doi:10.1115/1.3204586Serrano, J. R., Olmeda, P., Páez, A., & Vidal, F. (2010). An experimental procedure to determine heat transfer properties of turbochargers. Measurement Science and Technology, 21(3), 035109. doi:10.1088/0957-0233/21/3/035109Bohn, D., Heuer, T., & Kusterer, K. (2005). Conjugate Flow and Heat Transfer Investigation of a Turbo Charger. Journal of Engineering for Gas Turbines and Power, 127(3), 663-669. doi:10.1115/1.1839919Galindo, J., Luján, J. M., Serrano, J. R., Dolz, V., & Guilain, S. (2006). Description of a heat transfer model suitable to calculate transient processes of turbocharged diesel engines with one-dimensional gas-dynamic codes. Applied Thermal Engineering, 26(1), 66-76. doi:10.1016/j.applthermaleng.2005.04.010Sirakov, B., & Casey, M. (2012). Evaluation of Heat Transfer Effects on Turbocharger Performance. Journal of Turbomachinery, 135(2). doi:10.1115/1.4006608Serrano, J., Olmeda, P., Arnau, F., Reyes-Belmonte, M., & Lefebvre, A. (2013). Importance of Heat Transfer Phenomena in Small Turbochargers for Passenger Car Applications. SAE International Journal of Engines, 6(2), 716-728. doi:10.4271/2013-01-0576Larsson, P.-I., Westin, F., Andersen, J., Vetter, J., & Zumeta, A. (2009). Efficient turbo charger testing. MTZ worldwide, 70(7-8), 16-21. doi:10.1007/bf03226965Aghaali, H., & Ångström, H.-E. (2012). Turbocharged SI-Engine Simulation With Cold and Hot-Measured Turbocharger Performance Maps. Volume 5: Manufacturing Materials and Metallurgy; Marine; Microturbines and Small Turbomachinery; Supercritical CO2 Power Cycles. doi:10.1115/gt2012-68758Leufven, O., & Eriksson, L. (2012). Investigation of compressor correction quantities for automotive applications. International Journal of Engine Research, 13(6), 588-606. doi:10.1177/146808741243901

Towards virtual machine energy-aware cost prediction in clouds

Pricing mechanisms employed by different service providers significantly influence the role of cloud computing within the IT industry. With the increasing cost of electricity, Cloud providers consider power consumption as one of the major cost factors to be maintained within their infrastructures. Consequently, modelling a new pricing mechanism that allow Cloud providers to determine the potential cost of resource usage and power consumption has attracted the attention of many researchers. Furthermore, predicting the future cost of Cloud services can help the service providers to offer the suitable services to the customers that meet their requirements. This paper introduces an Energy-Aware Cost Prediction Framework to estimate the total cost of Virtual Machines (VMs) by considering the resource usage and power consumption. The VMs’ workload is firstly predicted based on an Autoregressive Integrated Moving Average (ARIMA) model. The power consumption is then predicted using regression models. The comparison between the predicted and actual results obtained in a real Cloud testbed shows that this framework is capable of predicting the workload, power consumption and total cost for different VMs with good prediction accuracy, e.g. with 0.06 absolute percentage error for the predicted total cost of the VM

Parameter estimation of the kinetic α-Pinene isomerization model using the MCSfilter algorithm

This paper aims to illustrate the application of a derivative-free multistart algorithm with coordinate search filter, designated as the MCSFilter algorithm. The problem used in this study is the parameter estimation problem of the kinetic α -pinene isomerization model. This is a well known nonlinear optimization problem (NLP) that has been investigated as a case study for performance testing of most derivative based methods proposed in the literature. Since the MCSFilter algorithm features a stochastic component, it was run ten times to solve the NLP problem. The optimization problem was successfully solved in all the runs and the optimal solution demonstrates that the MCSFilter provides a good quality solution.(undefined)info:eu-repo/semantics/publishedVersio

The DSM-5: hyperbole, hope or hypothesis?

The furore preceding the release of the new edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) is in contrast to the incremental changes to several diagnostic categories, which are derived from new research since its predecessor’s birth in 1990. While many of these changes are indeed controversial, they do reflect the intrinsic ambiguity of the extant literature. Additionally, this may be a mirror of the frustration of the field’s limited progress, especially given the false hopes at the dawn of the “decade of the brain”. In the absence of a coherent pathophysiology, the DSM remains no more than a set of consensus based operationalized adjectives, albeit with some degree of reliability. It does not cleave nature at its joints, nor does it aim to, but neither does alternate systems. The largest problem with the DSM system is how it’s used; sometimes too loosely by clinicians, and too rigidly by regulators, insurers, lawyers and at times researchers, who afford it reference and deference disproportionate to its overt acknowledged limitations

Robust Structured Low-Rank Approximation on the Grassmannian

Over the past years Robust PCA has been established as a standard tool for reliable low-rank approximation of matrices in the presence of outliers. Recently, the Robust PCA approach via nuclear norm minimization has been extended to matrices with linear structures which appear in applications such as system identification and data series analysis. At the same time it has been shown how to control the rank of a structured approximation via matrix factorization approaches. The drawbacks of these methods either lie in the lack of robustness against outliers or in their static nature of repeated batch-processing. We present a Robust Structured Low-Rank Approximation method on the Grassmannian that on the one hand allows for fast re-initialization in an online setting due to subspace identification with manifolds, and that is robust against outliers due to a smooth approximation of the $\ell_p$-norm cost function on the other hand. The method is evaluated in online time series forecasting tasks on simulated and real-world data

Temperature time series forecasting in The Optimal Challenges in Irrigation (TO CHAIR)

Predicting and forecasting weather time series has always been a difficult field of research analysis with a very slow progress rate over the years. The main challenge in this project—The Optimal Challenges in Irrigation (TO CHAIR)—is to study how to manage irrigation problems as an optimal control problem: the daily irrigation problem of minimizing water consumption. For that it is necessary to estimate and forecast weather variables in real time in each monitoring area of irrigation. These time series present strong trends and high-frequency seasonality. How to best model and forecast these patterns has been a long-standing issue in time series analysis. This study presents a comparison of the forecasting performance of TBATS (Trigonometric Seasonal, Box-Cox Transformation, ARMA errors, Trend and Seasonal Components) and regression with correlated errors models. These methods are chosen due to their ability to model trend and seasonal fluctuations present in weather data, particularly in dealing with time series with complex seasonal patterns (multiple seasonal patterns). The forecasting performance is demonstrated through a case study of weather time series: minimum air temperature.publishe

A management architecture for active networks

In this paper we present an architecture for network and applications management, which is based on the Active Networks paradigm and shows the advantages of network programmability. The stimulus to develop this architecture arises from an actual need to manage a cluster of active nodes, where it is often required to redeploy network assets and modify nodes connectivity. In our architecture, a remote front-end of the managing entity allows the operator to design new network topologies, to check the status of the nodes and to configure them. Moreover, the proposed framework allows to explore an active network, to monitor the active applications, to query each node and to install programmable traps. In order to take advantage of the Active Networks technology, we introduce active SNMP-like MIBs and agents, which are dynamic and programmable. The programmable management agents make tracing distributed applications a feasible task. We propose a general framework that can inter-operate with any active execution environment. In this framework, both the manager and the monitor front-ends communicate with an active node (the Active Network Access Point) through the XML language. A gateway service performs the translation of the queries from XML to an active packet language and injects the code in the network. We demonstrate the implementation of an active network gateway for PLAN (Packet Language for Active Networks) in a forty active nodes testbed. Finally, we discuss an application of the active management architecture to detect the causes of network failures by tracing network events in time

Activity Recognition and Prediction in Real Homes

In this paper, we present work in progress on activity recognition and prediction in real homes using either binary sensor data or depth video data. We present our field trial and set-up for collecting and storing the data, our methods, and our current results. We compare the accuracy of predicting the next binary sensor event using probabilistic methods and Long Short-Term Memory (LSTM) networks, include the time information to improve prediction accuracy, as well as predict both the next sensor event and its mean time of occurrence using one LSTM model. We investigate transfer learning between apartments and show that it is possible to pre-train the model with data from other apartments and achieve good accuracy in a new apartment straight away. In addition, we present preliminary results from activity recognition using low-resolution depth video data from seven apartments, and classify four activities - no movement, standing up, sitting down, and TV interaction - by using a relatively simple processing method where we apply an Infinite Impulse Response (IIR) filter to extract movements from the frames prior to feeding them to a convolutional LSTM network for the classification.Comment: 12 pages, Symposium of the Norwegian AI Society NAIS 201