Dual Bayesian Interpretation I am reading a book which says there are two ways of interpreting Bayes. 

“In the Bayesian approach, parameters can be viewed from two
  perspectives. Either we view the parameters as truly varying, or we
  view our knowledge about the parameters as imperfect. The fact that we
  obtain different estimates of parameters from different studies can be
  taken to reflect either of these two views.”
“In the first case, we understand the parameters of interest as
  varying – taking on different values in each of the samples we pick .
  For example, suppose that we conduct a blood test on an individual in
  two consecutive weeks, and represent the correlation between the red
  and white cell count as a parameter of our statistical model. Due to
  the many factors that affect the body’s metabolism, the count of each
  cell type will vary somewhat randomly, and hence the parameter value
  may vary over time. In the second case, we view our uncertainty over a
  parameter’s value as the reason we estimate slightly different values
  in different samples.  Bayesians are more at ease in using parameters
  as a means to an end – taking them not as real immutable constants,
  but as tools to help make inferences about a given situation.”

I dont get the second point, in the first due to the different conditions the parameter varies every time we measure. What does "we view our uncertainty over a parameter’s value as the reason we estimate slightly different values in different samples." mean? We measure different values each time, but what does this have to do with our certainty? Is he talking about our prediction probability? The yes we keep updating but that also reflects the distribution over the different conditions. 
He then gives an example where frequentist makes sense but there is also a Bayes interpretation which I am not clear on:

“For example, if our parameter represented the probability that an
  individual taken at random from the UK population has dyslexia, it is
  reasonable to assume that there is a true, or fixed, population value
  of the parameter in question. While the Frequentist view may be
  reasonable here, the Bayesian view can also handle this situation. In
  Bayesian statistics these parameters can be assumed fixed, but that we
  are uncertain of their value (here the true prevalence of dyslexia)
  before we measure them, and use a probability distribution to reflect
  this uncertainty.”

Is he saying here that in a sample of 10, I would estimate 0% dyslexia, then in a 100, maybe it becomes 3% and in 1000 like 5%. I think the frequentist interpretation is apt here. 
(Taken from Ben Lambert's 'A student's guide to Bayesian Statistics')
 A: The main thing to consider is that the frequentist and Bayesian approaches give different answers depending on the specific question.
I will now interpret the dyslexia scenario in different ways, hopefully showing that there is no one approach but a family of approaches (which may or may not make sense to you depending on your application).
Interpretation 1 (Frequentist)
I have a population of $N$ people, of which $n$ have dyslexia. I need to estimate the ratio $n/N$ from a sample $M$ of the population. Note that, in this case, the true answer is $n/N$, and simply observing all the people (that is, taking $M=N$ without replacement), we have the true and unique and correct answer.
Interpretation 2 (Frequentist)
Each time a person is born, there is a probability $\alpha$ that this person is affected by dyslexia. The current population is a sample of the people-generating process and we can use it to estimate the probability $\alpha$. Note that, in this case, the true probability $\alpha$ is not measurable as even collecting all the people and measuring $n/N$ will not give exactly $\alpha$.
Interpretation 3 (Bayesian)
I believe that 1/10 people have some form of dyslexia. Given a sample of $M$ people (of which $m$ have dyslexia) from this population of $N$ people, how many people have dyslexia in the population? (Note that, also in this case, the Bayesian answer is $n$ if I have access to $M=N$ without replacement.)
Interpretation 4 (Bayesian)
I believe that the probability of a newborn having dyslexia is 0.1. Then, I observe $m$ people with dyslexia in a sample of $M$ people, what is my belief now?
