= How do I calculate and interpret conditional probabilities? =

Gigerenzer (2002) suggests a way to obtain conditional probabilities using frequencies in a decision tree. An illustrated example (Wininger and Johnson, 2018) using this method in prosthetics is [[attachment:cond_prob.pdf | is here]].

Cortina and Dunlap (1997) give an example evaluating the detection rate of a test (positive/negative result) to detect schizophrenia (disorder).

To do this one fixes the following:

The base rate of schizophrenia in adults (2%)

The test will correctly identify schizophrenia (give a positive result) on 95% of people with schizophrenia

The test will correctly identify normal individuals (give a negative result) on 97% of normal people. 

Despite this we can show the [[attachment:bayes.doc|test is unreliable]].

This is a more intuitive way of illustrating the equivalent Bayesian equation:

$$\mbox{P(No disorder|+ result) = }\frac{\mbox{P(No disorder) * P(+ result | No disorder)}}{\mbox{P(No disorder) * P(+ result | No disorder) + P(Disorder) * P(- result | Disorder)}}$$

A talk with subtitles further illustrating aspects of conditional probabilities given by Ted Donnelly (Oxford), a geneticist, is available for viewing [[http://blog.ted.com/2006/11/statistician_pe.php|here.]]

 * [[attachment:bayes2.doc|More on Bayes theorem:Illustration of priors and likelihoods]]

__Using statistical distributions of likelihoods and priors to obtain posterior distributions__

Baguley (2012, p.393-395) gives formulae for the posterior mean ($$u_text{post}$$) and variance ($$\sigma_text{post}^text{2}$$) 

for a normal distribution, of form 

N(u, sigma^2 ^), 
with an assumed prior distribution of form 
N(u_p, sigma_p^2 ^) and an obtained likelihood distribution (obtained using sample data) equal to a 
N(u_lik, sigma_lik^2 ^). In particular

sigma_post^2 ^ =  [ 1 /sigma_lik^2 ^ + 1 /sigma_p^2^ ] ^-1^

 u_post = (sigma_post^2^ / sigma_lik^2^ ) u_lik + (sigma_post^2 ^ /  sigma_p^2 ^) u_p 

Zoltan Dienes also has a comprehensive website featuring a range of on-line Bayesian calculators including one that will evaluate posterior means and sds for Normal distributions [[http://www.lifesci.sussex.ac.uk/home/Zoltan_Dienes/inference/Bayes.htm | here.]]

Baguley also gives references for obtaining posterior distributions for data having a binomial distribution which assumes a beta distribution as its prior distribution. For this reason the posterior distribution, in this case, is called a beta-binomial distribution.

WINBUGS is freeware for fitting a range of models using simulation (via the Gibbs sampler) and is available from [[http://www.mrc-bsu.cam.ac.uk/bugs/winbugs/contents.shtml | here.]]

 * [[http://rsos.royalsocietypublishing.org/content/1/3/140216 | Using conditional probabilities to compute False Discovery Rates (article)]]

__References__

Andrews M and Baguley T (2013) Prior approval: The growth of Bayesian methods in psychology ''British Journal of Mathematical and Statistical  Psychology'' '''66(1)''' 1–7. Primer article free on-line to CBSU users.

Baguley T (2012) Serious Stats. A guide to advanced statistics for the behavioral sciences. Palgrave Macmillan:New York.

Cortina JM, Dunlap WP (1997) On the logic and purpose of significance testing. ''Psychological Methods'' '''2(2)''' 161-172.

Gelman A and Shalizi CR (2013) Philosophy and the practice of Bayesian statistics ''British Journal of Mathematical and Statistical Psychology'' '''66(1)''' 8–38. Primer article free to access on-line to CBSU users.

Gigerenzer G (2002) Reckoning with risk: learning to live with uncertainty. London: Penguin.

Krushchk JK (2011) Doing bayesian data analysis: a tutorial using R and BUGS. Academic Press:Elsevier. For further reading: genuinely accessible to beginners illustrating using prior and posterior probabilities in inference for ANOVAs and other regression models.

Wininger M and Johnson R (2018) Prosthetic hand signals:how Bayesian inference can decode movement intentions and control the next generation of powered prostheses. ''Significance''
'''15(4)''' 30-35.