applied environmental statisticspeople.bu.edu/dietze/bayes2018/lesson01_intro.pdf · what is a...
TRANSCRIPT
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AppliedEnvironmental
Statistics
GE 509
Instructor: Prof. Michael Dietze
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Introductions
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LM
Most environmental dataviolates the assumptionsof these tests
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What is statisticalmodeling?
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What is statisticalmodeling?
“Confronting models with data”● Model fitting / parameter estimation
● Model comparison
● Estimation, partitioning, and propagation of uncertainties
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What is statisticalmodeling?
“Confronting models with data”
Design the statistical analysis to fitthe data rather than the data to fitthe test
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What is a model?
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What is a model?
A conceptual, graphical, or mathematicalrepresentation / abstraction of someempirical process(es).
A mathematical function that formalizesour conceptual model / theory
f (x)=a f (x)=
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What is a model?
Models areHYPOTHESES
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Syllabus
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Course Materials
● Reading assignments, lecture slides, projectdetails, etc. are all posted on the lab websitehttp://people.bu.edu/dietze/Bayes2018/GE509.htm
● Primary Text:“Models for Ecological Data”Clark 2007 Princeton U Press
● Software:● R / RStudio● OpenBUGS / JAGS● Git / GitHub
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GradingGrading will be based on lab reports, a semester-long project, and four exams.
Lab reports/problem sets (10 points each) = 150Semester project (GRAD) = 95
project proposal 2/9 (10)model description 3/2 (15)preliminary analysis 4/9 (20)Final report before exam 4 (50)
Exams (30, 25, 30, 30 points ) = 115[non-cumulative]
Total = 360
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Labs
● LAB IS MANDATORY
● Labs will be posted in git repositoryhttps://github.com/mdietze/EE509
● Due FOLLOWING WEEK by the start of lab
● Must be turned in individually● Can work together
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Semester Project
● Final product: “Journal article” on a data analysis● You choose topic● ENCOURAGED to use your own data● Analysis must be new, use concepts from class● “Methods” heavy
● Four milestones● One lab is peer critique
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Lecture & Exams
● Four sections● Probability theory and Maximum Likelihood● Bayesian methods● Hierarchical/mixed models
– Linear regression → nonlinear, non-gaussian● Advanced topics
– Time series– Spatial
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Exams
● Multiple Choice● Matching● Fill in the blank● Short Answer / Derivation● ~15 questions
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Expectations
● You have seen basic calculus at some point● Primarily need to follow derivations
● Basic familiarity with statistical concepts● e.g. experimental design, randomization, mean,
median, variance
● Open mind● You will work hard● You won't 'get' Bayes the first time they see it
(but will need to by the 2nd exam)
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Objectives
● Literacy● Read and evaluate advanced stats used in papers
● Proficiency● underlying statistical concepts● Software: R, JAGS
● Exposure to advanced topics● Paradigm shift
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A bit more on motivation....
Data are usually complex
Violate the assumptions of classical tests
This complexity can be addressed with moderntechniques
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Example: How much light is a tree getting?
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Example: How much light is a tree getting?
Intermediate
Dominant
Suppressed
FIELD
MODEL
REMOTE SENSING
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FIELD MODEL
R.S.
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Linear models
Logistic
Multinomial
FIELD MODEL
R.S.
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Problem Characteristics
● Multiple data constraints● Non-linear relationships● Non-Normal residuals● Non-constant variance● Latent variables (response variable
not being observed directly)● Distinction between observation error
and process variability● Missing data
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Statistical Paradigms
● Classical (e.g. sum of squares)
● Maximum Likelihood
● Bayesian
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Statistical Paradigms
StatisticalEstimator
Method ofEstimation
Output DataComplexity
PriorInfo
Classical Cost Function AnalyticalSolution
Point Estimate Simple No
MaximumLikelihood
ProbabilityTheory
NumericalOptimization
Point Estimate Intermediate No
Bayesian ProbabilityTheory
Sampling ProbabilityDistribution
Complex Yes
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Statistical Paradigms
StatisticalEstimator
Method ofEstimation
Output DataComplexity
PriorInfo
Classical Cost Function AnalyticalSolution
Point Estimate Simple No
MaximumLikelihood
ProbabilityTheory
NumericalOptimization
Point Estimate Intermediate No
Bayesian ProbabilityTheory
Sampling ProbabilityDistribution
Complex Yes
The unifying principal for this course isstatistical estimation based on probability
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Next lecture
● Will cover basics of probability theory● Read
● Clark 2007 - Chapter 1● Hilborn and Mangel p39-62 (course website)
● Optional● Clark 2007 – Appendix D (Probability)● Otto and Day – Appendix 1 (Math) and 2 (Calculus)
(course website)