1. 1 Bayesian methods for parameter estimation and data assimilation with crop models Part 2: Likelihood function and prior distribution David Makowski and Daniel Wallach INRA, France October 2006

2. 2 Notions in probability - Joint probability - Conditional probability - Marginal probability Bayes’ theorem Previously

3. 3 Objectives of part 2 Introduce the notion of prior distribution. Introduce the notion of likelihood function. Show how to estimate parameters with a Bayesian method.

4. 4 Part 2: Likelihood function and prior distributions Estimation of parameters (  ) Parameter = numerical value not calculated by the model and not observed. Information available to estimate parameters - A set of observations (y). - Prior knowledge about parameter values.

5. 5 Part 2: Likelihood function and prior distributions Two distributions in Bayes’ theorem Likelihood function = function relating data to parameters. Prior parameter distribution = probability distribution describing our initial knowledge about parameter values.

6. 6 Part 2: Likelihood function and prior distributions Measurements Prior Information about parameter values Bayesian method Combined info about parameters

7. 7 Example Estimation of crop yield θ by combining a measurement with expert knowledge. Measurement y = 9 t/ha ± 1 about 5 t/ha ± 2 Expert Field with unknown yield  Part 2: Likelihood function and prior distributions Plot

8. 8 Example Estimation of crop yield θ by combining a measurement with expert knowledge. Part 2: Likelihood function and prior distributions One parameter to estimate: the crop yield θ. Two types of information available: - A measurement equal to 9 t/ha with a standard error equal to 1 t/ha. - An estimation provided by an expert equal to 5 t/ha with a standard error equal to 2 t/ha.

9. 9 Part 2: Likelihood function and prior distributions Prior distribution It describes our belief about the parameter values before we observe the measurements. It is based on past studies, expert knowledge, and litterature.

10. 10 Part 2: Likelihood function and prior distributions Example (continued) Definition of a prior distribution θ ~ N( µ,  ² ) Normal probability distribution. Expected value equal to 5 t/ha. Standard error equal to 2 t/ha

11. 11 Part 2: Likelihood function and prior distributions Example (continued) Plot of the prior distribution

12. 12 Part 2: Likelihood function and prior distributions Likelihood function A likelihood function is a function relating data to parameters. It is equal to the probability that the measurements would have been observed given some parameter values. Notation: P(y | θ)

13. 13 Part 2: Likelihood function and prior distributions Example (continued) Statistical model y | θ ~ N( θ, σ² ) y = θ +  with  ~ N( 0, σ² )

14. 14 Part 2: Likelihood function and prior distributions Example (continued) Definition of a likelihood function Normal probability distribution. Measurement y assumed unbiaised and equal to 9 t/ha. Standard error σ assumed equal to 1 t/ha y | θ ~ N( θ, σ² )

15. 15 Part 2: Likelihood function and prior distributions Example (continued) Definition of a likelihood function Maximum likelihood estimate

16. 16 Part 2: Likelihood function and prior distributions Maximum likelihood Likelihood functions are also used by frequentist to implement the maximum likelihood method. The maximum likelihood estimator is the value of θ maximizing P(y | θ).

17. 17 Part 2: Likelihood function and prior distributions Likelihood function Prior probability distribution

18. 18 Part 2: Likelihood function and prior distributions Example (continued) Analytical expression of the posterior distribution θ | y ~ N( µ post,  post ² )

19. 19 Part 2: Likelihood function and prior distributions Prior probability distributionLikelihood function Posterior probability distribution

20. 20 1.Result is a probability distribution (posterior distr.) 2.Posterior mean is intermediate between prior mean and observation. 3.Weight of each depends on prior variance and measurement error. 4.Posterior variance is lower than both prior variance and measurement error variance. 5.Used just one data point and still got estimator. Part 2: Likelihood function and prior distributions Example (continued) Discussion of the posterior distribution

21. 21 Frequentist versus Bayesian Part 2: Likelihood function and prior distributions Bayesian analysis introduces an element of subjectivity: the prior distribution. But its representation of the uncertainty is easy to understand - the uncertainty is assessed conditionally to the observations, - the calculations are straightforward when the posterior distribution is known.

22. 22 Part 2: Likelihood function and prior distributions Which is better? Bayesian methods often lead to - more realistic estimated parameter values, - in some cases, more accurate model predictions. Problems when prior information is wrong and when one has a strong confidence in it.

23. 23 Part 2: Likelihood function and prior distributions Difficulties for estimating crop model parameters Which likelihood function ? - Unbiaised errors ? - Independent errors ? Which prior distribution ? - What do the parameters really represent ? - Level of uncertainty ? - Symmetric distribution ?

24. 24 Part 2: Likelihood function and prior distributions Practical considerations The analytical expression of the posterior distribution can be derived for simple applications. For complex problems, the posterior distribution must be approximated.

25. 25 Next part Importance sampling, an algorithm to approximate the posterior probability distribution.