Step 2: Creating the Safety Observation Process

2004 ◽  
pp. 68-93
Keyword(s):  
Observation Process

scholarly journals Parameter estimation for energy balance models with memory

2014 ◽  
Vol 470 (2169) ◽  
pp. 20140349 ◽  
Author(s):  
Lionel Roques ◽  
Mickaël D. Chekroun ◽  
Michel Cristofol ◽  
Samuel Soubeyrand ◽  
Michael Ghil
Keyword(s):  
Parameter Estimation ◽  
Energy Balance ◽  
Statistical Model ◽  
Ice Cores ◽  
Temperature Measurements ◽  
Age Dating ◽  
Observation Process ◽  
Wide Range ◽  
Energy Balance Models ◽  
With Memory

We study parameter estimation for one-dimensional energy balance models with memory (EBMMs) given localized and noisy temperature measurements. Our results apply to a wide range of nonlinear, parabolic partial differential equations with integral memory terms. First, we show that a space-dependent parameter can be determined uniquely everywhere in the PDE's domain of definition D , using only temperature information in a small subdomain E ⊂ D . This result is valid only when the data correspond to exact measurements of the temperature. We propose a method for estimating a model parameter of the EBMM using more realistic, error-contaminated temperature data derived, for example, from ice cores or marine-sediment cores. Our approach is based on a so-called mechanistic-statistical model that combines a deterministic EBMM with a statistical model of the observation process. Estimating a parameter in this setting is especially challenging, because the observation process induces a strong loss of information. Aside from the noise contained in past temperature measurements, an additional error is induced by the age-dating method, whose accuracy tends to decrease with a sample's remoteness in time. Using a Bayesian approach, we show that obtaining an accurate parameter estimate is still possible in certain cases.

The Observation Process

2018 ◽  
pp. 5-8
Author(s):  
Edward K. Yeargers
Keyword(s):  
Observation Process

Optimal Control of the Observation Process

1996 ◽  
pp. 509-542
Author(s):  
V. N. Afanas’ev ◽  
V. B. Kolmanovskii ◽  
V. R. Nosov
Keyword(s):  
Optimal Control ◽  
Observation Process

scholarly journals Formal Classroom Observations: Factors That Affect Their Success

2017 ◽  
Vol 10 (6) ◽  
pp. 83
Author(s):  
Zeba Zaidi
Keyword(s):  
Performance Feedback ◽  
Online Survey ◽  
English Language ◽  
Educational Institution ◽  
Educational Institutions ◽  
Whole Process ◽  
Observation Process ◽  
Class Room ◽  
The City ◽  
Formal Class

Formal class room observation is a very delicate topic in any educational institution. It involves a series of emotions and sentiments that come with the package. In this paper, the researcher will attempt to analyze the factors that affect the process in a relatively significant manner and thereby contribute greatly to the success or failure of the whole process. The researcher will also attempt to explore the various aspects of the process at two tertiary level educational institutions and how they can be controlled in order to maintain the purpose of the process as developmental and constructive rather than a critical, judgmental and/or negative outlook, which eventually defeats the whole idea of classroom observation for performance feedback and growth. The data was collected at two renowned English Language Institutes (ELIs) in the city of Jeddah, Saudi Arabia through an online survey comprising of ten questions including one open-ended question. After analyzing the gathered data, conclusions were formulated and certain suggestive measures were proposed that can benefit the observers to look at the observation process in a better light. It will also help them accomplish the objectives of the process in a more prolific manner and thereby, contribute in achieving a more conscious and thorough professional development of the faculty on the whole.

scholarly journals Dynamic Credit Quality Evaluation with Social Network Data

2019 ◽  
Vol 2019 ◽  
pp. 1-11
Author(s):  
Stanley Sewe ◽  
Philip Ngare ◽  
Patrick Weke
Keyword(s):  
Social Network ◽  
Continuous Time ◽  
Network Data ◽  
Linear Quadratic ◽  
Arrival Times ◽  
Network Information ◽  
Credit Quality ◽  
Social Network Data ◽  
Observation Process ◽  
Time Observation

We investigate the filtering problem where the borrower’s time varying credit quality process is estimated using continuous time observation process and her (in this paper we refer to the borrower as female and the lender as male) ego-network data. The hidden credit quality is modeled as a hidden Gaussian mean-reverting process whilst the social network is modeled as a continuous time latent space network model. At discrete times, the network data provides unbiased estimates of the current credit state of the borrower and her ego-network. Combining the continuous time observed behavioral data and network information, we provide filter equations for the hidden credit quality and show how the network information reduces information asymmetry between the borrower and the lender. Further, we consider the case when the network information arrival times are random and solve stochastic optimal control problem for a lender having linear quadratic utility function.

Filters and parameter estimation for a partially observable system subject to random failure with continuous-range observations

2004 ◽  
Vol 36 (4) ◽  
pp. 1212-1230 ◽  
Author(s):  
Daming Lin ◽  
Viliam Makis
Keyword(s):  
Condition Monitoring ◽  
Continuous Time ◽  
The State ◽  
Parameter Estimates ◽  
State Process ◽  
Observation Process ◽  
Special Cases ◽  
Random Failure ◽  
Prediction Problems ◽  
Continuous Range

We consider a failure-prone system operating in continuous time. Condition monitoring is conducted at discrete time epochs. The state of the system is assumed to evolve as a continuous-time Markov process with a finite state space. The observation process with continuous-range values is stochastically related to the state process, which, except for the failure state, is unobservable. Combining the failure information and the condition monitoring information, we derive a general recursive filter, and, as special cases, we obtain recursive formulae for the state estimation and other quantities of interest. Updated parameter estimates are obtained using the expectation-maximization (EM) algorithm. Some practical prediction problems are discussed and finally an illustrative example is given using a real dataset.

A Bayesian state–space mark–recapture model to estimate exploitation rates in mixed-stock fisheries

2006 ◽  
Vol 63 (2) ◽  
pp. 321-334 ◽  
Author(s):  
Catherine G.J Michielsens ◽  
Murdoch K McAllister ◽  
Sakari Kuikka ◽  
Tapani Pakarinen ◽  
Lars Karlsson ◽  
...  
Keyword(s):  
State Space ◽  
Expert Knowledge ◽  
Mortality Rates ◽  
Fishing Effort ◽  
Model Parameters ◽  
Fishing Mortality ◽  
Mark Recapture ◽  
Observation Process ◽  
Mixed Stock ◽  
Reporting Rates

A Bayesian state–space mark–recapture model is developed to estimate the exploitation rates of fish stocks caught in mixed-stock fisheries. Expert knowledge and published results on biological parameters, reporting rates of tags and other key parameters, are incorporated into the mark–recapture analysis through elaborations in model structure and the use of informative prior probability distributions for model parameters. Information on related stocks is incorporated through the use of hierarchical structures and parameters that represent differences between the stock in question and related stocks. Fishing mortality rates are modelled using fishing effort data as covariates. A state–space formulation is adopted to account for uncertainties in system dynamics and the observation process. The methodology is applied to wild Atlantic salmon (Salmo salar) stocks from rivers located in the northeastern Baltic Sea that are exploited by a sequence of mixed- and single-stock fisheries. Estimated fishing mortality rates for wild salmon are influenced by prior knowledge about tag reporting rates and salmon biology and, to a limited extent, by prior assumptions about exploitation rates.

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