The Effect of Parameter Uncertainties in an Age–Length Relationship upon Estimating the Age Composition of Catches

1983 ◽  
Vol 40 (3) ◽  
pp. 272-280 ◽  
Author(s):  
J. Majkowski ◽  
J. Hampton
Keyword(s):  
Sensitivity Analysis ◽  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
Probability Distributions ◽  
Bluefin Tuna ◽  
Parameter Uncertainties ◽  
Age Classes ◽  
Age Composition ◽  
Length Relationship ◽  
Southern Bluefin Tuna

A simple, but frequently applied, procedure for decomposing fish length frequencies into age-classes is considered. This decomposition consists of converting fish lengths into ages using an age–length relationship. A method for assessing the effect of parameter uncertainties in this relationship upon estimates of the age composition of catches is presented. It is assumed that the parameter uncertainties can be described by probability distributions. Our aim is to determine probability distributions of age composition estimates resulting from these uncertainties. This is done using a stochastic sensitivity analysis technique involving Monte Carlo simulations and/or a first-order theory if such a theory is valid in the case under consideration. The method is illustrated by its application to data from the southern bluefin tuna (Thunnus maccoyii) fishery. It is found that simulated (Monte Carlo) catch estimates for age-classes 3 (fish at age 2–3 yr) to 13 (fish at age 12–13 yr) are normally distributed. The coefficients of variation of these estimates are less than 12%. Simulated catch estimates for age-classes 1, 2, and 14–20 deviate considerably from normality and their ranges bounded by the 2.5 and 97.5 percentiles are extremely wide; they include values different by up to 810% from the best deterministic catch estimates.Key words: catch, age composition, uncertainties, sensitivity analysis, Monte Carlo simulations, southern bluefin tuna

Mortality Estimation: Biased Results from Unbiased Ages

1984 ◽  
Vol 41 (12) ◽  
pp. 1843-1847 ◽  
Author(s):  
Jay Barlow
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
Age Estimation ◽  
Age Determination ◽  
Mortality Rates ◽  
Age Classes ◽  
Estimation Errors ◽  
Mortality Estimation ◽  
Age Estimates

Estimates of mortality rates from age distributions are biased by imprecision in age estimation, even if age estimates are unbiased. I have derived a method for predicting the magnitude of this bias from information on the precision of age determination. Monte Carlo simulations show that bias can be accurately predicted. The commonly used Chapman–Robson mortality estimator is shown to be robust to imprecision in age determination if all age-classes are included. Errors are likely, however, if one or more age-classes are excluded or if other mortality estimators are used. Biases can be corrected if the distribution of age-estimation errors is known.

Comparison of Monte Carlo and Fuzzy Math Simulation Methods for Quantitative Microbial Risk Assessment

2003 ◽  
Vol 66 (10) ◽  
pp. 1900-1910 ◽  
Author(s):  
VALERIE J. DAVIDSON ◽  
JOANNE RYKS
Keyword(s):  
Risk Assessment ◽  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
Interval Arithmetic ◽  
Food Systems ◽  
Probability Distributions ◽  
Economic Cost ◽  
Fuzzy Mathematics ◽  
Quantitative Microbial Risk Assessment ◽  
Mean Values

The objective of food safety risk assessment is to quantify levels of risk for consumers as well as to design improved processing, distribution, and preparation systems that reduce exposure to acceptable limits. Monte Carlo simulation tools have been used to deal with the inherent variability in food systems, but these tools require substantial data for estimates of probability distributions. The objective of this study was to evaluate the use of fuzzy values to represent uncertainty. Fuzzy mathematics and Monte Carlo simulations were compared to analyze the propagation of uncertainty through a number of sequential calculations in two different applications: estimation of biological impacts and economic cost in a general framework and survival of Campylobacter jejuni in a sequence of five poultry processing operations. Estimates of the proportion of a population requiring hospitalization were comparable, but using fuzzy values and interval arithmetic resulted in more conservative estimates of mortality and cost, in terms of the intervals of possible values and mean values, compared to Monte Carlo calculations. In the second application, the two approaches predicted the same reduction in mean concentration (−4 log CFU/ml of rinse), but the limits of the final concentration distribution were wider for the fuzzy estimate (−3.3 to 5.6 log CFU/ml of rinse) compared to the probability estimate (−2.2 to 4.3 log CFU/ml of rinse). Interval arithmetic with fuzzy values considered all possible combinations in calculations and maximum membership grade for each possible result. Consequently, fuzzy results fully included distributions estimated by Monte Carlo simulations but extended to broader limits. When limited data defines probability distributions for all inputs, fuzzy mathematics is a more conservative approach for risk assessment than Monte Carlo simulations.

Comparison of methods for estimating age composition with application to Southern Bluefin Tuna (Thunnus maccoyii)

2008 ◽  
Vol 93 (1-2) ◽  
pp. 22-28 ◽  
Author(s):  
Richard Morton ◽  
Mark Bravington
Keyword(s):  
Bluefin Tuna ◽  
Comparison Of Methods ◽  
Age Composition ◽  
Southern Bluefin Tuna

scholarly journals Diffusion of oxygen interstitials in UO2+ using kinetic Monte Carlo simulations: Role of O/M ratio and sensitivity analysis

2016 ◽  
Vol 472 ◽  
pp. 89-98 ◽  
Author(s):  
Rakesh K. Behera ◽  
Taku Watanabe ◽  
David A. Andersson ◽  
Blas P. Uberuaga ◽  
Chaitanya S. Deo
Keyword(s):  
Sensitivity Analysis ◽  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
Kinetic Monte Carlo ◽  
Kinetic Monte Carlo Simulations

scholarly journals Deterministic Sensitivity Analysis for First-Order Monte Carlo Simulations: A Technical Note

2009 ◽  
Vol 12 (1) ◽  
pp. 96-97 ◽  
Author(s):  
Benjamin P. Geisler ◽  
Uwe Siebert ◽  
G. Scott Gazelle ◽  
David J. Cohen ◽  
Alexander Göhler
Keyword(s):  
Sensitivity Analysis ◽  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
Technical Note ◽  
First Order ◽  
Deterministic Sensitivity Analysis

scholarly journals SINH-ACCELERATION: EFFICIENT EVALUATION OF PROBABILITY DISTRIBUTIONS, OPTION PRICING, AND MONTE CARLO SIMULATIONS

2019 ◽  
Vol 22 (03) ◽  
pp. 1950011 ◽  
Author(s):  
SVETLANA BOYARCHENKO ◽  
SERGEI LEVENDORSKIĬ
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
Regime Switching ◽  
Probability Distributions ◽  
General Scheme ◽  
Characteristic Exponent ◽  
Heston Model ◽  
Characteristic Functions ◽  
Quadratic Term ◽  
Quadratic Term Structure

Characteristic functions of several popular classes of distributions and processes admit analytic continuation into unions of strips and open coni around [Formula: see text]. The Fourier transform techniques reduce calculation of probability distributions and option prices in the evaluation of integrals whose integrands are analytic in domains enjoying these properties. In the paper, we suggest to use changes of variables of the form [Formula: see text] and the simplified trapezoid rule to evaluate the integrals accurately and fast. We formulate the general scheme, and apply the scheme for calculation probability distributions and pricing European options in Lévy models, the Heston model, the CIR model, and a Lévy model with the CIR-subordinator. We outline applications to fast and accurate calibration procedures and Monte Carlo simulations in Lévy models, regime switching Lévy models that can account for stochastic drift, volatility and skewness, the Heston model, other affine models and quadratic term structure models. For calculation of quantiles in the tails using the Newton or bisection method, it suffices to precalculate several hundreds of values of the characteristic exponent at points on an appropriate grid (conformal principal components) and use these values in formulas for cpdf and pdf.

Sensitivity analysis for oblique incidence reflectometry using Monte Carlo simulations

2015 ◽  
Vol 54 (23) ◽  
pp. 7099 ◽  
Author(s):  
Faisal Kamran ◽  
Peter E. Andersen
Keyword(s):  
Sensitivity Analysis ◽  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
Oblique Incidence

scholarly journals Goal-oriented sensitivity analysis for lattice kinetic Monte Carlo simulations

2014 ◽  
Vol 140 (12) ◽  
pp. 124108 ◽  
Author(s):  
Georgios Arampatzis ◽  
Markos A. Katsoulakis
Keyword(s):  
Sensitivity Analysis ◽  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
Kinetic Monte Carlo ◽  
Kinetic Monte Carlo Simulations
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