FishBase goes FishBayes R, JAGS and Bayesian Statistics

Slides:

Advertisements

Similar presentations

Progress with FishBase Data and Tools for Stock Assessment Rainer Froese ECOKNOWS, Heraklion.

Advertisements

Biodiversity of Fishes Length-Weight Relationships Rainer Froese ( )

Biodiversity of Fishes Understanding Longevity Rainer Froese

Statistics : Statistical Inference Krishna.V.Palem Kenneth and Audrey Kennedy Professor of Computing Department of Computer Science, Rice University 1.

From Data to Best Available Knowledge New Developments in FishBase Rainer Froese, GEOMAR, 12 th FishBase Symposium Big Old Data and Shiny.

Growth in Length and Weight Rainer Froese SS 2008.

ECOKNOWS WP6 Progress Nov Rainer Froese Cadiz,

Descriptive statistics Experiment  Data  Sample Statistics Experiment  Data  Sample Statistics Sample mean Sample mean Sample variance Sample variance.

Barents Sea fish modelling in Uncover Daniel Howell Marine Research Institute of Bergen.

Lesson 12-1 Algebra Check Skills You’ll Need 12-4

1 Bayesian methods for parameter estimation and data assimilation with crop models Part 2: Likelihood function and prior distribution David Makowski and.

Additional Slides on Bayesian Statistics for STA 101 Prof. Jerry Reiter Fall 2008.

ECE 8443 – Pattern Recognition LECTURE 06: MAXIMUM LIKELIHOOD AND BAYESIAN ESTIMATION Objectives: Bias in ML Estimates Bayesian Estimation Example Resources:

Foundations of Sociological Inquiry The Logic of Sampling.

FIN Progress Report for ECOKNOWS Rainer Froese, Rudy Reyes Rennes, France, 3 February 2014 Skype Presentation 1.

Bayesian inference review Objective –estimate unknown parameter  based on observations y. Result is given by probability distribution. Bayesian inference.

FishBase goes FishBayes R, JAGS and Bayesian Statistics Rainer Froese FIN Seminar, 21 February 2013 Kush Hall, IRRI, Los Baños, Philippines.

Bayesian Networks for Data Mining David Heckerman Microsoft Research (Data Mining and Knowledge Discovery 1, (1997))

ECE 8443 – Pattern Recognition LECTURE 07: MAXIMUM LIKELIHOOD AND BAYESIAN ESTIMATION Objectives: Class-Conditional Density The Multivariate Case General.

Mixture Models, Monte Carlo, Bayesian Updating and Dynamic Models Mike West Computing Science and Statistics, Vol. 24, pp , 1993.

The nature of science. Scientific knowledge is the product of observation and inference. Observations and Inferences.

Statistics PSY302 Quiz One Spring A _____ places an individual into one of several groups or categories. (p. 4) a. normal curve b. spread c.

Analyzing Research Data and Presenting Findings

8 Sampling Distribution of the Mean Chapter8 p Sampling Distributions Population mean and standard deviation,  and   unknown Maximal Likelihood.

- 1 - Bayesian inference of binomial problem Estimating a probability from binomial data –Objective is to estimate unknown proportion (or probability of.

Biodiversity of Fishes Size Matters ( ) Rainer Froese.

Chapter 10: Introduction to Statistical Inference.

1 BA 275 Quantitative Business Methods Quiz #3 Statistical Inference: Hypothesis Testing Types of a Test P-value Agenda.

1 BA 275 Quantitative Business Methods Quiz #2 Sampling Distribution of a Statistic Statistical Inference: Confidence Interval Estimation Introduction.

Unit 1 Sections 1-1 & : Introduction What is Statistics?  Statistics – the science of conducting studies to collect, organize, summarize, analyze,

Rainer Froese GEOMAR Presentation at the FishBase Symposium

Psychology 202a Advanced Psychological Statistics November 12, 2015.

- We have samples for each of two conditions. We provide an answer for “Are the two sample means significantly different from each other, or could both.

One Sample Mean Inference (Chapter 5)

Chapter 18: The Central Limit Theorem Objective: To apply the Central Limit Theorem to the Normal Model CHS Statistics.

FishBase goes FishBayes Summarizing all available information for all fishes Rainer Froese 14th FishBase Symposium 2nd September 2013, Thessaloniki, Greece.

Statistics Sampling Distributions and Point Estimation of Parameters Contents, figures, and exercises come from the textbook: Applied Statistics and Probability.

Bayesian Approach Jake Blanchard Fall Introduction This is a methodology for combining observed data with expert judgment Treats all parameters.

1 Guess the Covered Word Goal 1 EOC Review 2 Scientific Method A process that guides the search for answers to a question.

Statistical Methods. 2 Concepts and Notations Sample unit – the basic landscape unit at which we wish to establish the presence/absence of the species.

FishBase goes FishBayes New Approaches toward Best Available Knowledge Rainer Froese iMarine Workshop, 15 May 2013 DG Connect, Brussels, Belgium.

Parameter Estimation. Statistics Probability specified inferred Steam engine pump “prediction” “estimation”

Observations and Inferences

Rainer Froese GEOMAR 41st CIESM Congress Audimax, University of Kiel

Bayesian data analysis

Variance and Standard Deviation Confidence Intervals

Section 1: Estimating with large samples

Rainer Froese GEOMAR 14th FishBase Symposium

Sampling Distributions for a Proportion

MSFD Indicators and Reference Points for Data-Limited Stocks

The Scientific Method.

Biodiversity of Fishes Understanding Longevity

Biodiversity of Fishes Length-Weight Relationships

Inference Concerning a Proportion

Biodiversity of Fishes Size Matters ( )

Statistics PSY302 Review Quiz One Fall 2018

OVERVIEW OF BAYESIAN INFERENCE: PART 1

BIOLOGY SEPTEMBER 2013 Opening Assignments.

FIN Progress Report for ECOKNOWS

Section 9.3 Distribution of Sample Means

LECTURE 07: BAYESIAN ESTIMATION

Sampling Distribution Models

From Data to Best Available Knowledge New Developments in FishBase

Statistics PSY302 Review Quiz One Spring 2017

MSFD Indicators and Reference Points for Data-Limited Stocks

Tools of Environmental Science

75 previous answer What is of 37.5? ? go to.

75 previous answer What is of 60? ? go to.

Observations information gathered by our senses.

Presentation transcript:

FishBase goes FishBayes R, JAGS and Bayesian Statistics Rainer Froese FIN Seminar, 21 February 2013 Kush Hall, IRRI, Los Baños, Philippines

Problem Statement FishBase has compiled thousands of studies on growth, maturity, reproduction, diet How can the information be summarized? How can new studies be informed? How can best estimates for species without studies be derived? Answer: Bayesian Statistics

Bayesian Inference in a Nutshell Prior: express existing knowledge (textbook, common sense, logic, best guess, previous studies) with a central value (such as a mean) and a distribution around it (such as a normal distribution and a standard deviation). Likelihood function: analyze new data, get the mean and distribution Posterior: Combine prior and likelihood into a new, intermediate mean and distribution

Example: Length Weight Relationships

Example: Length Weight Relationships

Example: LWR Across All Studies

Example: LWR for Many Studies

Example: LWR for One Study Only

Example: LWR Priors

Example: FishBase Online

Example: FishBase Online

Example: FishBase Online

Example: FishBase Online

Example: FishBase Online

Example: FishBase Online (after about 5 minutes...)

Example: FishBase Online

Example: FishBase Online

Example: FishBase Online

Example: FishBase Online

Next Steps Assign LWR to all species (32,000) Repeat exercise with growth estimates (ongoing) Repeat exercise with mortality and maturity Estimate intrinisc rate of population increase (the holy grail in biology)

Questions?