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Tags

#bayes #programming #r #statistics

Question

What happens if the posterior predictions do not mimic the given data?

Answer

Check that the posterior predictions mimic the data with reasonable accuracy (i.e., conduct a “posterior predictive check”). If not, then consider a different descriptive model

Tags

#bayes #programming #r #statistics

Question

What happens if the posterior predictions do not mimic the given data?

Answer

?

Tags

#bayes #programming #r #statistics

Question

What happens if the posterior predictions do not mimic the given data?

Answer

Check that the posterior predictions mimic the data with reasonable accuracy (i.e., conduct a “posterior predictive check”). If not, then consider a different descriptive model

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#### Parent (intermediate) annotation

**Open it**

e Bayesian inference to re-allocate credibility across parameter values. Interpret the posterior distribution with respect to theoretically meaningful issues (assuming that the model is a reasonable description of the data; see next step). 5. <span>Check that the posterior predictions mimic the data with reasonable accuracy (i.e., conduct a “posterior predictive check”). If not, then consider a different descriptive model<span><body><html>

#### Original toplevel document (pdf)

owner: shihabdider - (no access) - Doing Bayesian Data Analysis, p27

e Bayesian inference to re-allocate credibility across parameter values. Interpret the posterior distribution with respect to theoretically meaningful issues (assuming that the model is a reasonable description of the data; see next step). 5. <span>Check that the posterior predictions mimic the data with reasonable accuracy (i.e., conduct a “posterior predictive check”). If not, then consider a different descriptive model<span><body><html>

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
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repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

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