# Assessing Confidence the Bayesian Way

Week 8 | 4.1

### LEARNING OBJECTIVES

*After this lesson, you will be able to:*

- Understand the mathematics behind the credible interval
- Apply your previous learnings on Bayesian statistics in a formal 'decision process' for statistical estimation
- Understand the difference between credible intervals and confidence intervals

### STUDENT PRE-WORK

*Before this lesson, you should already be able to:*

- Understand how to to simple hypothesis testing, including constructing classical confidence intervals
- Explain conceptually posterior distributions, and basic engage in basic Bayesian statistics covered earlier in the week

### INSTRUCTOR PREP

*Before this lesson, instructors will need to:*

- Gather materials needed for class
- Read through datasets and starter/solution code
- Add to the "Additional Resources" section for this lesson

### LESSON GUIDE

Timing | Type | Topic |
---|---|---|

5 min | Opening | A Review of Classical Confidence Intervals |

10 min | Introduction | Introducing Statistical Testing - Bayesian Way |

15 min | Demo | Blast from the past - Confidence Intervals |

25 min | Guided Practice | Credible Intervals |

25 min | Independent Practice | Assessing Bayesian Results |

5 min | Conclusion | Concluding Remarks |

## A Review of Classical Confidence Intervals (5 min)

As you recall, statistical testing forms the foundations of many statistical "decision making processes". The point is to define some value: A difference of quantities, A quotient of quantities etc.. This quantity is usually a population parameter which can be nicely analyzed under an assumed distribution (usually normality).

Once we have a distribution, we can build the confidence band around the quantity that tells us ... tells us what? Well, this is where we need operate with care, cause the formal definition of the confidence band is as follows:

If we assume a certain confidence level $\alpha$, then $\alpha$ % of the confidence intervals will "include" the quantity within the intervals lower and upper bounds.

It's sort of like playing a game of horseshoes. For those of you outside of the United States (or Texas), horseshoes is a game where people throw a horse shoe at a small metal poll, hoping to get the shoe around the poll (picture below). This is pretty much how you need to think about the confidence bands. For those of us who are not equestrians, we luckily have an analog to the classical view of confidence within the Bayesian purview.

## Introducing Statistical Testing - Bayesian Way (10 mins)

Like previously, we will show the equivalent Bayesian formalism for the confidence interval. As you recall, in many cases, we can reconstruct the classical statistic (regression, model etc.) by constructing a non-informative posterior distribution. We usually like to show this equivalency because it better demonstrates that Bayesian statics can be thought of as a more general approach to statistical analysis. Whereby, we can include prior information to tune results more towards "reality" or the local data, but barring that extra information, we arrive at similar or equivalent results to the established classical literature.

To review, a posterior distribution look like the following:

```
$$ \begin{equation} \pi(\theta|x_1, x_2, ..., x_n ) = \dfrac{f(x| \theta_i )\pi(\theta)}{\sum\limits_{i=0}^{n} f(x| \theta_i)(\pi(\theta_i)}
\end{equation}$$
```

### The Mathematical Predicates for the Credibility Intervals

A non-informative prior is one where the prior is selected to be "uniform" or otherwise not impact the formula in a way with "information" extracted from either the modelers opinions or from data itself. In this case, we define the non-informative prior to a uniformly valued function and we can assumed the likelihood function as the following:

`$$ f(\mu|x_i)=\prod_{i = 1}^{n}frac{1}{(2\pi\alpha_{i}^{2})^{1/2}} \exp\frac{(\mu-x_i)^2}{2\alpha_{x}^{2}} $$`

So what are we doing here? We're assuming the likelihood is normally distributed, and since we are assuming non-informativity, this makes the numerator much simpler, and in fact as we have already proved, by setting our prior to this, we will get the identical results as the classical confidence interval.

## Blast from the past - Confidence Intervals (15 mins)

To better hammer in the point of the similarity, let's go ahead and review and construct a classical confidence interval. As you recall, we learn about the confidence interval when we need to discern whether an effect we have observed from an experiment is "statistically significant". From the above commentary on the convoluted nature of the interpretation of these results, we know that there is a bit of linguistic legerdemain required in "accurately" reporting results to the experimenter and/or any stakeholder.

In fact, in most cases, especially in business, the ultimate consumer of your analytic (be it higher levels of management, executives, or clients) will probably misinterpret/misunderstand what is actually being said to them. This issue with communication will be more pronounced in places like the United States, where quantitative education (or "numeracy" as the British call it), is quite poor "on the mean".

Under these circumstances, the more "common sense" interpretation of Bayesian statistics may help you communicate your results more accurately and clearly in plain english, while still leveraging the data-visualization power of classical statistics (i.e histograms, etc.)

Anyway, to resume, recall that we want to leverage 'nice' properties about population parameters (mean, medians etc.) for creating our confidence interval, in this case, statisticians/researchers often also leverage the assumption of normality so they can make produce results about the variance as well.

So the confidence interval often looks something like this for the 95 percentile:

`$$ (x +/- 2\alpha_{\sigma}) $$`

Again, the 95th percentile is 2 standard deviations from the estimated mean (or sample mean). This concept can further be encapsulated into a single under the guise of the p-value (Although, this practice has been increasingly being discouraged in recent times given the disturbing prevalence of 'p-hacking'). The p-value is in itself just a conditional probability that states: if we assume that we have the correct mean, distribution etc., what is the probability that we would observe the value we observed in the data/experiment.

As a review in-code, let's build a quick confidence interval so we can be sure of ourselves that we know how it works:

```
# Assuming normality, construct the confidence interval
from scipy.stats import t
from numpy import average, std
from math import sqrt
# Create a random vector of 30 numbers that can range from 0 to 100 - you can just use a uniform distribution to select the numbers.
Data_vec = ...
# Compute the mean of this vector
Mean_for_data = average(Data_vec)
# Compute the standard deviation (or variance and take the sqrt of that)
SDev_for_data = std(Data_vec, ddof=1)
# Compute the classical confidence interval
Confidence_for_data = [Mean_for_data + val * SDev_for_data / sqrt(len(Data_vec)) for val in t.interval(0.95, len(Data_vec) - 1)]
```

## Credible Intervals (25 mins)

Now that we are assured our understanding of the classical results, and how to implement them in python, let's move on towards developing an equivalent Bayesian perspective in Python.

As previously with our Bayesian analysis, we need to do some mathematics to get to the result. Our non-informative predicate prevents the prior-distribution from making our numerator more complex than it has too. Therefore, resolving the posterior distribution will be more or less reduced to resolving the likelihood function. Yet, the likelihood function is itself a product of normally distributed variables... a monstrosity to disentangle mathematically.

However, if we recall, the best trick to solve this using MLE (or MAP). After a bunch of calculus and solving systems of equations, we get the following closed form as the solution:

`$$\sigma_2=\sigma_1 + \frac{log(1-.95)}{len(data)}$$`

Where, `$\sigma_1$`

is the minimum value of the sample.

So our job is to code that up into Python:

```
# Take your random vector of data and compute the min - We are using the numpy methods for brevity/clarity
Min_vec = Data_vec.min()
# Compute the second sigma thus defining your upper bound
sigma_2 = Min_vec + np.log(.05)/len(Min_vec)
```

## Assessing Bayesian Results (25 min)

Take the same random vectors (make sure it's locked in first by setting a seed or setting another array equal to your random vector call in python) used in the previous exercises and compare the results of the confidence band, are they similar? If they aren't, exactly the same what can be the cause of this? How different do you think the results would be if we changed the prior ?

```
# Work here
```

## Conclusion (5 min)

We've gotten our feet wet with the Bayesian credibility interval, the net step will be to apply this to data and work closer with some Python library methods to make the computation easier/streamlined.