Above are papers related to the following paper: [Paper_ID] Paper #1103016. 	[Title] Inference in Dynamic Error-in-Variable-Measurement Problems [Abstract] Efficient algorithms have been developed for estimating model parameters from measured data, even in the presence of gross errors. In addition to point estimates of parameters, however, assessments of uncertainty are needed. Linear approximations provide standard errors, but these can be misleading when applied to models that are substantially nonlinear. To overcome this difficulty, "profiling" methods have been developed for the case in which the regressor variables are error free. In this paper we extend profiling methods to Error-in-Variable-Measurement (EVM) models. We use Laplace's method to integrate out the incidental parameters associated with the measurement errors, and then apply profiling methods to obtain approximate confidence contours for the parameters. This approach is computationally efficient, requiring few function evaluations, and can be applied to large scale problems. It is useful when the certain measurement errors (e.g., input variables) are relatively small, but not so small that they can be ignored. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #14083. 	[Title] Data Reconciliation and Gross Error Detection for Dynamic Systems [Abstract] Gross error detection plays a vital role in parameter estimation and data reconciliation for both dynamic and steady state systems. In particular, recent advances in process optimization now allow data reconciliation of dynamic systems and appropriate problem formulations need to be considered for them. Data errors due to either miscalibrated or faulty sensors or just random events nonrepresentative of the underlying statistical distribution, can induce heavy biases in the parameter estimates and in the reconciled data. In this paper we concentrate on robust estimators and exploratory statistical methods which allow us to detect the gross errors as the data reconciliation is performed. These robust methods have the property of being insensitive to departures from ideal statistical distributions and therefore are insensitive to the presence of outliers. Once the regression is done, the outliers can be detected readily by using exploratory statistical techniques. An important feature for performance of the optimization algorithm and uniqueness of the reconciled data is the ability to classify the variables according to their observability and redundancy properties. Here an observable variable is an unmeasured quantity which can be estimated from the measured variables through the physical model while a nonredundant variable is a measured variable which cannot be estimated other than through its measurements. Variable classification can be used as an aid to design instrumentation schemes. In this Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #14090. 	[Title] Subregion-Adaptive Integration of Functions Having a Dominant Peak [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #62676. 	[Title] Nonsmooth Dynamic Simulation With Linear Programming Based Methods [Abstract] Process simulation has emerged as a valuable tool for process design, analysis and operation. In this work, we extend the capabilities of iterated linear programming (LP) for dealing with problems encountered in dynamic nonsmooth process simulation. A previously developed LP method is refined with the addition of a new descent strategy which combines line search with a trust region approach. This adds more stability and efficiency to the method. The LP method has the advantage of naturally dealing with profile bounds as well. This is demonstrated to avoid the computational difficulties which arise from the iterates going into physically unrealistic regions. A new method for the treatment of discontinuities occurring in dynamic simulation problems is also presented in this paper. The method ensures that any event which has occurred within the time interval in consideration is detected and if more than one event occurs, the detected one is indeed the earliest one. A specific class of implicitly discontinuous process simulation problems, phase equilibrium calculations is also looked at. A new formulation is introduced to solve multiphase problems. fl To whom all correspondence should be addressed. email:biegler@cmu.edu Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #643069. 	[Title] Markov Chain Monte Carlo in Practice: A Roundtable Discussion [Abstract] Markov chain Monte Carlo (MCMC) methods make possible the use of flexible Bayesian models that would otherwise be computationally infeasible. In recent years, a great variety of such applications have been described in the literature. Applied statisticians who are new to these methods may have several questions and concerns, however: How much effort and expertise are needed to design and use a Markov chain sampler? How much confidence can one have in the answers that MCMC produces? How does the use of MCMC affect the rest of the model-building process? At the Joint Statistical Meetings in August, 1996, a panel of experienced MCMC users discussed these and other issues, as well as various "tricks of the trade". This paper is an edited recreation of that discussion. Its purpose is to offer advice and guidance to novice users of MCMC and to not-so-novice users as well. Topics include building confidence in simulation results, methods for speeding convergence, assessing standard errors, identification of models for which good MCMC algorithms exist, and the current state of software development. Please summarise all the given information.

Paper #1103016 has the title `Inference in Dynamic Error-in-Variable-Measurement Problems  
` and the abstract `Efficient algorithms have been developed for estimating model parameters from measured data, even in the presence of gross errors. In addition to point estimates of parameters, however, assessments of uncertainty are needed. Linear approximations provide standard errors, but these can be misleading when applied to models that are substantially nonlinear. To overcome this difficulty, "profiling" methods have been developed for the case in which the regressor variables are error free. In this paper we extend profiling methods to Error-in-Variable-Measurement (EVM) models. We use Laplace's method to integrate out the incidental parameters associated with the measurement errors, and then apply profiling methods to obtain approximate confidence contours for the parameters. This approach is computationally efficient, requiring few function evaluations, and can be applied to large scale problems. It is useful when the certain measurement errors (e.g., input variables) are relatively small, but not so small that they can be ignored. 
`.
Question: Which category should Paper #1103016 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #1103016 has the title `Inference in Dynamic Error-in-Variable-Measurement Problems  
` and the abstract `Efficient algorithms have been developed for estimating model parameters from measured data, even in the presence of gross errors. In addition to point estimates of parameters, however, assessments of uncertainty are needed. Linear approximations provide standard errors, but these can be misleading when applied to models that are substantially nonlinear. To overcome this difficulty, "profiling" methods have been developed for the case in which the regressor variables are error free. In this paper we extend profiling methods to Error-in-Variable-Measurement (EVM) models. We use Laplace's method to integrate out the incidental parameters associated with the measurement errors, and then apply profiling methods to obtain approximate confidence contours for the parameters. This approach is computationally efficient, requiring few function evaluations, and can be applied to large scale problems. It is useful when the certain measurement errors (e.g., input variables) are relatively small, but not so small that they can be ignored. 
`.
Question: Which category should Paper #1103016 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].

This paper should be classified into: Neural_Networks