Consistency of System Identification by Global Total Least Squares

Global total least squares (GTLS) is a method for the identification of linear systems where no distinction between input and output variables is required. This method has been developed within the deterministic behavioural approach to systems. In this paper we analyse statistical properties of this method when the observations are generated by a multivariable stationary stochastic process. In particular, sufficient conditions for the consistency of GTLS are derived. This means that, when the number of observations tends to infinity, the identified deterministic system converges to the system that provides an optimal appoximation of the data generating process. The two main results are the following. GTLS is consistent if a guaranteed stability bound can be given a priori. If this information is not available, then consistency is obtained (at some loss of finite sample efficiency) if GTLS is applied to the observed data extended with zero values in past and future

Behavioural Approximation of Stochastic Processes by Rank Reduced Spectra

Behaviours provide an elegant, parameter free characterization of deterministic systems. We discuss a possible application of behaviours in the approximation of stochastic systems. This can be seen as an extension to the dynamic case of the well-known static factor analysis model. An essential difference is that we see modelling primarily as a matter of process approximation, not as a method to recover the true data generating process. In particular we see "noise properties" as a kind of prior model assumption that can be compared with the resulting quality of the process approximation

Dirac Fields in Loop Quantum Gravity and Big Bang Nucleosynthesis

Big Bang nucleosynthesis requires a fine balance between equations of state for photons and relativistic fermions. Several corrections to equation of state parameters arise from classical and quantum physics, which are derived here from a canonical perspective. In particular, loop quantum gravity allows one to compute quantum gravity corrections for Maxwell and Dirac fields. Although the classical actions are very different, quantum corrections to the equation of state are remarkably similar. To lowest order, these corrections take the form of an overall expansion-dependent multiplicative factor in the total density. We use these results, along with the predictions of Big Bang nucleosynthesis, to place bounds on these corrections.Comment: 15 pages, 2 figures; v2: new discussion of relevance of quantum gravity corrections (Sec. II) and new estimates (Sec. V

Identification of System Behaviours by Approximation of Time Series Data

The behavioural framework has several attractions to offer for the identification of multivariable systems. Some of the variables may be left unexplained without the need for a distinction between inputs and outputs; criteria for model quality are independent of the chosen parametrization; and behaviours allow for a global (i.e., non-local) approximation of the system dynamics. This is illustrated with a behavioural least squares method with an application in dynamic factor analysis

Consistency of global total least squares in stochastic system identification

Global total least squares has been introduced as a method for the identification of deterministic system behaviours. We analyse this method within a stochastic framework, where the observed data are generated by a stationary stochastic process. Conditions are formulated so that the method is consistent in the sense that, when the number of observations tends to infinity, the identified deterministic behaviour converges to the behaviour that provides an optimal appoximation of the data generating process

Behavioural Approximation of Stochastic Processes by Rank Reduced Spectra

Behaviours provide an elegant, parameter free characterization of deterministic systems. We discuss a possible application of behaviours in the approximation of stochastic systems. This can be seen as an extension to the dynamic case of the well-known static factor analysis model. An essential difference is that we see modelling primarily as a matter of process approximation, not as a method to recover the true data generating process. In particular we see "noise properties" as a kind of prior model assumption that can be compared with the resulting quality of the process approximation.factor analysis;behaviours;least squares;lineair systems;stationary processes

Evaluating movements of opakapaka (Pristipomoides filamentosus) relative to a restricted fishing area by using acoustic telemetry and a depth-constrained estimator of linear home ranges

Networks of no-take fishery reserves have emerged as a tool for managing deepwater fish species. In Hawaii and elsewhere, such areas are used to manage deepwater snapper species. However, little is known regarding the movements of these species relative to protected areas. We used passive acoustic telemetry to track crimson jobfish (Pristipomoides filamentosus), also known as opakapaka, in one of Hawaii’s bottomfish restricted fishing areas to understand the size required for a reserve to protect this species. From January 2017 through January 2018, 179 fish were tagged. Only 10 fish were classified as alive on the basis of movements indicated by detections in tracking data (tracks). For these fish, the median time between the first and last detection of an individual on an acoustic receiver array was 414.5 d with a mean number of detections per individual of 28,321. Linear estimates of home range averaged 3.7 and 6.0 km in conservative and optimistic scenarios, smaller than the median linear habitat dimension of Hawaii’s reserves. Fish were detected within the reserve on 97% or more of the days they were tracked. These results indicate that current reserves in Hawaii are likely sufficient in scale to confer positive biological benefits to opakapaka that reside within their borders

System Identification by Dynamic Factor Models

This paper concerns the modelling of stochastic processes by means of dynamic factor models. In such models the observed process is decomposed into a structured part called the latent process, and a remainder that is called noise. The observed variables are treated in a symmetric way, so that no distinction between inputs and outputs is required. This motivates the condition that also the prior assumptions on the noise are symmetric in nature. One of the central questions in this paper is how uncertainty about the noise structure translates into non-uniqueness of the possible underlying latent processes. We investigate several possible noise specifications and analyse properties of the resulting class of observationally equivalent factor models. This concerns in particular the characterization of optimal models and properties of continuity and consistency

On Random Bubble Lattices

We study random bubble lattices which can be produced by processes such as first order phase transitions, and derive characteristics that are important for understanding the percolation of distinct varieties of bubbles. The results are relevant to the formation of topological defects as they show that infinite domain walls and strings will be produced during appropriate first order transitions, and that the most suitable regular lattice to study defect formation in three dimensions is a face centered cubic lattice. Another application of our work is to the distribution of voids in the large-scale structure of the universe. We argue that the present universe is more akin to a system undergoing a first-order phase transition than to one that is crystallizing, as is implicit in the Voronoi foam description. Based on the picture of a bubbly universe, we predict a mean coordination number for the voids of 13.4. The mean coordination number may also be used as a tool to distinguish between different scenarios for structure formation.Comment: several modifications including new abstract, comparison with froth models, asymptotics of coordination number distribution, further discussion of biased defects, and relevance to large-scale structur