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http://ashfieldguesthouse.co.uk/special-offers/ http://tripmag.co.uk/autosave-file-vom-d-lab23-der-agfaphoto-gmbh-18/ Course Length:    5 Days           see url Course Dates:      http://lowestoftelectricalgroup.co.uk/james-paget-university-hospital-gorleston/ August 3 – 7, 2020             follow link Course Venue:        Calgary, Alberta, Canada


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go to link This course highlights core principles quantitative risk analysis and the most important modeling principles, methods and techniques. This course will be taught using the R statistical software package & other risk modeling tools. It will focus on how to conduct accurate and effective quantitative risk analyses, including best practices of risk modeling, selecting the appropriate distribution, using data and expert opinion, and avoiding common mistakes. Many practical group exercises will reinforce the concepts introduced throughout the course


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enter site This course is designed for reservoir engineers, geologists, geophysicists, project managers, asset managers, senior managers and those with professional interest to perform quantitative risk analysis in petroleum finance, project risk analysis, engineering design and operations, among others.


get link Buy Diazepam Online Europe What You Will Learn:

  • Understand required fundamental methodologies to effectively assess uncertainty and risks. Understand essential probability and statistics theory and various stochastic processes as related to quantitative risk analysis
  • Understand the core principles of quantitative core principles of quantitative risk analysis and most important risk modeling principles
  • How to conduct accurate and effective quantitative risk analyses including best practices of Risk Modeling employing R Software package and other quantitative risk modeling tools such as @Risk, CrystalBall & other simulation tools
  • Learn to think more probabilistically and promote the use of rigorous risk analysis
  • Understand the value of portfolio analysis and risk optimization in E & P projects

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  • Background of risk analysis and risk management. Risk analysis as a team effort. Decision tools by transforming data to knowledge. Dealing with the limits of sparse data sets. Introduction to probability theory. Basics of risk modeling. Workflows for Building Risk Analysis
  • Overview of R statistical software package. Risk modeling workflows in R and other statistical software package. Introduction to analyzing and using data for risk analysis
  • Stochastic processes – the basis of risk analysis. The use of Bayesian statistics in risk analysis
  • General good practices in risk modeling. Common mistakes and how to prevent them
  • Introduction to risk management. Risk management processes. Workflows for Managing Risks
  • Case Studies. Reflection & Overall Summary