Papers by Carlos Braumann
The Quarterly Review of Biology, Sep 1, 1979
Preface xi About the companion website xv 1 Introduction 1 2 Revision of probability and stochast... more Preface xi About the companion website xv 1 Introduction 1 2 Revision of probability and stochastic processes 9 2.1 Revision of probabilistic concepts 9 2.2 Monte Carlo simulation of random variables 25 2.3 Conditional expectations, conditional probabilities, and independence 29 2.4 A brief review of stochastic processes 35 2.5 A brief review of stationary processes 40 2.6 Filtrations, martingales, and Markov times 41 2.7 Markov processes 45 3 An informal introduction to stochastic differential equations 51
... Patrıcia Bermudez Paula Brito Paula Vicente Paulo MM Rodrigues Paulo Soares Pedro Nascimento ... more ... Patrıcia Bermudez Paula Brito Paula Vicente Paulo MM Rodrigues Paulo Soares Pedro Nascimento ... fischeri Francisco Saias, Dulce G. Pereira, Ana M. Anselmo e Susana M. Paix ... Barreto-Hernandez, Lisete Sousa, Maria Antónia Amaral Turkman e Margarida Gama-Carvalho . . . ...
De Gruyter eBooks, Dec 31, 1999
Springer eBooks, 2020
The use of general descriptive names, registered names, trademarks, service marks, etc. in this p... more The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use.
Optimization
We apply a class of stochastic differential equations to model individual growth in a randomly fl... more We apply a class of stochastic differential equations to model individual growth in a randomly fluctuating environment using cattle weight data. We have used maximum likelihood theory to estimate the parameters. However, for cattle data, it is often not feasible to obtain animal's observations at equally spaced ages nor even at the same ages for different animals and there is typically a small number of observations at older ages. For these reasons, maximum likelihood estimates can be quite inaccurate, being interesting to consider in the likelihood function a weight function associated to the elapsed times between two consecutive observations of each animal, which results in the weighted maximum likelihood method. We compare the results obtained from both methods in several data structures and conclude that the weighted maximum likelihood improves the estimation when observations at older ages are scarce and the observation instants are unequally spaced, whereas the maximum likelihood estimates are recommended when animals are weighted at equally spaced ages. For unequally spaced observations, a bootstrap estimation method was also applied in order to correct the bias of the maximum likelihood estimates; it revealed to be a more precise alternative, except when the available data only has young animals.
Invited conference of the scientific meeting II Iberian Nathematic Meeting (Badajoz, Spain, Octob... more Invited conference of the scientific meeting II Iberian Nathematic Meeting (Badajoz, Spain, October 2008). The conference involved several applications of stochastic differential equations in population growth and animal individual growth, including the issues of extinction, existence of stationary densities and the use of Itô or Stratonovich calculus
Fisheries Research, 2019
In a previous paper, we discussed the use of an optimal variable effort fishing policy versus an ... more In a previous paper, we discussed the use of an optimal variable effort fishing policy versus an optimal sustainable constant effort fishing policy in terms of the expected accumulated discounted profit during a finite time interval. We concluded that there is only a slight reduction in profit when choosing the applicable optimal sustainable constant effort fishing policy instead of the optimal variable effort fishing policy, which leads to major disadvantages in practice. In this paper, we confirm these conclusions by considering a different model, the Gompertz model, and by using another realistic dataset of parameters and a more general profit structure. We also show that some of the disadvantages of the optimal variable effort fishing policy, namely those related to social objectives, are eliminated by considering a penalized profit with an artificial running energy cost on the effort. However, the applicability problems remain. We also show that the profit advantage of this optimal penalized variable effort policy over the optimal sustainable constant effort policy is even smaller than the already very small advantage of the non-penalized policy. This further reinforces the robustness of our previous conclusions that the optimal sustainable constant effort policy, which does not have the shortcomings of the optimal variable effort fishing policy, is only slightly less profitable than it.
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Papers by Carlos Braumann