Sensitivity Analysis in GEM-SA. GEM-SA course - session 62 Example ForestETP vegetation model 7 input parameters 120 model runs Objective: conduct a variance-based.

Slides:

Advertisements

Similar presentations

Factor: 7r 2 + 8r + 1 Factor the first term: 7r 2 = (7r) (r) 7rr Write them side by side with space The last term is positive and the middle term is positive.

Advertisements

Table de multiplication, division, addition et soustraction.

Slide 1 Insert your own content. Slide 2 Insert your own content.

Slide 1Fig. 20.1, p.607. Slide 2Table 20.1, p.608.

Rachel T. Johnson Douglas C. Montgomery Bradley Jones

STATISTICS Linear Statistical Models

STATISTICS INTERVAL ESTIMATION Professor Ke-Sheng Cheng Department of Bioenvironmental Systems Engineering National Taiwan University.

We need a common denominator to add these fractions.

1 SESSION 5 Graphs for data analysis. 2 Objectives To be able to use STATA to produce exploratory and presentation graphs In particular Bar Charts Histograms.

Grade D Number - Decimals – x x x x x – (3.6 1x 5) 9.

On Sequential Experimental Design for Empirical Model-Building under Interval Error Sergei Zhilin, Altai State University, Barnaul, Russia.

Excel Functions. Part 1. Introduction 2 An Excel function is a formula or a procedure that is performed in the Visual Basic environment, outside the.

RAIK 283: Data Structures & Algorithms

Parentheses- (), {}, [] Exponent- 2³ Multiply or Divide- which ever come first Add or subtract- which ever comes first.

1 1 Slide © 2003 South-Western/Thomson Learning TM Slides Prepared by JOHN S. LOUCKS St. Edwards University.

CE 498/698 and ERS 685 (Spring 2004) Lecture 61 Lecture 6: Feedback Systems of Reactors CE 498/698 and ERS 685 Principles of Water Quality Modeling.

1 IMDS Tutorial Integrated Microarray Database System.

3-5 Arithmetic Sequences as Linear Functions

Pivot Tables Overview 1. What are Pivot Tables Pivot tables in Excel are a versatile reporting tool that makes it easy to extract information from large.

Regression with Panel Data

Part 12: Linear Regression 12-1/27 Statistics and Data Analysis Professor William Greene Stern School of Business IOMS Department Department of Economics.

BSE-in-NX 2.0 Integrated Plug-in for NX Users BSE-in-NX 2.0 Feature Overview December 5, 2013.

2.5 Using Linear Models Month Temp º F 70 º F 75 º F 78 º F.

Uncertainty and Sensitivity Analysis of Complex Computer Codes

Lets play bingo!!. Calculate: MEAN Calculate: MEDIAN

Sensitivity Analysis in GEM-SA Jeremy Oakley. Example  ForestETP vegetation model – 7 input parameters – 120 model runs  Objective: conduct a variance-based.

Using an emulator. Outline So we’ve built an emulator – what can we use it for? Prediction What would the simulator output y be at an untried input x.

Multivariate Data/Statistical Analysis SC504/HS927 Spring Term 2008 Week 18: Relationships between variables: simple ordinary least squares (OLS) regression.

Insert Date HereSlide 1 Using Derivative and Integral Information in the Statistical Analysis of Computer Models Gemma Stephenson March 2007.

MaK_Full ahead loaded 1 Alarm Page Directory (F11)

Chapter 61 Processing the Client Request JavaServer Pages By Xue Bai.

Addition 1’s to 20.

Uncertainty Analysis Using GEM-SA Tony O’Hagan. Outline  Setting up the project  Running a simple analysis  Exercise  More complex analyses.

Getting started with GEM-SA Marc Kennedy. This talk  Starting GEM-SA program  Creating input and output files  Explanation of the menus, toolbars,

Slide 1 John Paul Gosling University of Sheffield GEM-SA: a tutorial.

Running a model's adjoint to obtain derivatives, while more efficient and accurate than other methods, such as the finite difference method, is a computationally.

Test B, 100 Subtraction Facts

A First Course Fifth Edition

2 x0 0 12/13/2014 Know Your Facts!. 2 x1 2 12/13/2014 Know Your Facts!

Bottoms Up Factoring. Start with the X-box 3-9 Product Sum

Tutorial 1: Sensitivity analysis of an analytical function

16. Mean Square Estimation

Section 5.6 Review Difference of Two Squares Sum & Difference of Two Cubes  Recognizing Perfect Squares  Difference of Two Squares  Recognizing Perfect.

9. Two Functions of Two Random Variables

Commonly Used Distributions

Multiplication Facts Practice

BA 555 Practical Business Analysis

Graeme Henchel Multiples Graeme Henchel

0 x x2 0 0 x1 0 0 x3 0 1 x7 7 2 x0 0 9 x0 0.

Exercise 7.5 (p. 343) Consider the hotel occupancy data in Table 6.4 of Chapter 6 (p. 297)

Applied Econometrics Second edition

Uncertainty Analysis Using GEM-SA. GEM-SA course - session 42 Outline Setting up the project Running a simple analysis Exercise More complex analyses.

Regression Analysis Using Excel. Econometrics Econometrics is simply the statistical analysis of economic phenomena Here, we just summarize some of the.

RESEARCH STATISTICS Jobayer Hossain Larry Holmes, Jr November 6, 2008 Examining Relationship of Variables.

Simple Linear Regression

1 1 Slide © 2004 Thomson/South-Western Slides Prepared by JOHN S. LOUCKS St. Edward’s University Slides Prepared by JOHN S. LOUCKS St. Edward’s University.

Using an emulator. Outline So we’ve built an emulator – what can we use it for? Prediction What would the simulator output y be at an untried input x?

Getting started with GEM-SA Marc Kennedy Central Science Laboratory, York Tony O’Hagan, Jeremy Oakley University of Sheffield.

Getting started with GEM-SA. GEM-SA course - session 32 This talk Starting GEM-SA program Creating input and output files Explanation of the menus, toolbars,

1 1 Slide © 2003 South-Western/Thomson Learning™ Slides Prepared by JOHN S. LOUCKS St. Edward’s University.

Using SPSS Note: The use of another statistical package such as Minitab is similar to using SPSS.

Using SPSS Note: The use of another statistical package such as Minitab is similar to using SPSS.

Chapter 3 Minitab Recipe Cards

Using SPSS Note: The use of another statistical package such as Minitab is similar to using SPSS.

Chapter 13 Excel Extension: Now You Try!

Chapter 9 Excel Extension: Now You Try!

Presentation transcript:

Sensitivity Analysis in GEM-SA

GEM-SA course - session 62 Example ForestETP vegetation model 7 input parameters 120 model runs Objective: conduct a variance-based sensitivity analysis to identify which uncertain inputs are driving the output uncertainty.

GEM-SA course - session 63 Exploratory scatter plots

GEM-SA course - session 64 Sensitivity analysis walkthrough 1.  Project  New 2. In the Files tab, click on Browse on the Inputs File row GEM-SA Demo Data / Model1 / emulator7x120inputs.txt 3. Click on Browse on the Outputs File row GEM-SA Demo Data / Model1 / out11.txt 4. Select the Options tab

GEM-SA course - session 65 Sensitivity analysis walkthrough 5. Change the Number of Inputs to Tick the calculate main effects and sum effects boxes only 7. Leave the other options unchanged Input uncertainty options: All unknown, uniform Prior mean options: Linear term for each input Generate predictions as: function realisations (correlated points)

GEM-SA course - session 66 Sensitivity analysis walkthrough

GEM-SA course - session 67 Sensitivity analysis walkthrough 8. Click OK 9. An Inputs Parameter Ranges window will appear. Click Defaults from input ranges, then OK 10.  Project  Run or use

GEM-SA course - session 68 Main effect plots

GEM-SA course - session 69 Main effect plots Fixing X 6 = 18, this point shows the expected value of the output (obtained by averaging over all other inputs). Simply fixing all the other inputs at their central values and comparing X 6 =10 with X 6 =40 would underestimate the influence of this input (The thickness of the band shows emulator uncertainty)

GEM-SA course - session 610 Variance of main effects Main effects for each input. Input 6 has the greatest individual contribution to the variance Main effects sum to 66% of the total variance

GEM-SA course - session 611 Interactions and total effects Main effects explain 2/3 of the variance Model must contain interactions Any input can have small main effect, but large interaction effect, so overall still an ‘important’ input Can ask GEM-SA to compute all pair-wise interaction effects 435 in total for a 30 input model – can take some time! Useful to know what to look for

GEM-SA course - session 612 Interactions and total effects For each input X i Total effect = main effect for X i + all interactions involving X i Main effects and total effects normalised by variance Total effect >> main effect implies interactions in the model Look for inputs with large total effects relative to main effects Investigate possible interactions involving those inputs

GEM-SA course - session 613 Interactions and total effects Total effects for inputs 4 and 7 much larger than its main effect. Implies presence of interactions

GEM-SA course - session 614 Interaction effects 11.  Project  Edit or 12. In Options tab, tick calculate joint effects 13. De-select all inputs under inputs to include in joint effects, select X4, X5, X6, X7

GEM-SA course - session 615 Interaction effects 14. Click OK 15.  Project  Run or

GEM-SA course - session 616 Interaction effects Note interactions involving inputs 4 and 7 Main effects and selected interactions now sum to almost 92% of the total variance

GEM-SA course - session 617 Exercise 1. Set up a new project using SAex1_inputs.txt for the inputs and SAex1_outputs.txt for the output 8 input parameters (uniform on [0,1]) 100 model runs 2. Estimate the main effects only for this model and identify the influential input variables 3. By comparing main effects with total effects, can you spot any interactions? 4. Estimate any suspected interactions to test your intuition!