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8 changes: 4 additions & 4 deletions src/bayesianstats.rst
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Expand Up @@ -41,7 +41,7 @@ Specifying a Prior for a Proportion

An appropriate prior to use for a proportion is a Beta prior.

For example, if you want to estimate the proportion of people like chocolate, you
For example, if you want to estimate the proportion of people who like chocolate, you
might have a rough idea that the most likely value is around 0.85, but that the proportion
is unlikely to be smaller than 0.60 or bigger than 0.95.

Expand Down Expand Up @@ -137,7 +137,7 @@ We can plot the prior density by using the "curve" function:

|image1|

Note that in the command above we use the "dbeta()" function to specify that
Note that in the command above we use the "dbeta()" function to specify
the density of a Beta(52.22,9.52105105105105) distribution.

We can see from the picture of the density for a Beta(52.22,9.52105105105105) distribution
Expand Down Expand Up @@ -216,7 +216,7 @@ the proportion, taking the data into consideration. That is, you may wish to cal
the conditional distribution of the proportion given the data and the prior. This is is called
the posterior distribution for the proportion.

The posterior distribution ssummarises what is known about the proportion after the data
The posterior distribution summarises what is known about the proportion after the data
has been observed, and combines the information from the prior and the data.

In our example of estimating the proportion of people who like chocolate, we have a Beta(52.22,9.52) prior
Expand Down Expand Up @@ -288,7 +288,7 @@ Since the prior and posterior are distributions, the area under their densities
The likelihood has been scaled so that the area underneath it is also 1, so that it is
easy to compare the likelihood with the prior and posterior.

Therefore, the prior and likelihood curves should look the same shape as those plotted
Therefore, the prior and likelihood curves should have the same shape as those plotted
before (see above), but the y-axis scale is different for the likelihood scale compared
to the plot made using calcLikelihoodForProportion() above.

Expand Down