Bayesian Statistics is a branch of Statistics that provides tools which help in understanding the probability of the occurrence of an event with respect to the new data introduced. This can also be understood as upgrading their beliefs, with the introduction of new data.
Bayesian Statistics can be understood as a particular approach, for executing the concept of probability, to the basic statistical problems. This implies that it acts as a mathematical tool which helps in strengthening our belief about certain events with respect to new data or new evidence concerning these events. This can simply be understood as the prediction of the outcome of events, when two events are supplementary to each other.
Hence, the inference (Bayesian) interprets the probability, which is a measure of conviction of, say, confidence that a particular person may possess with regard to the occurrence of the particular event.
A preconceived notion about the belief may exist about the event, but it again becomes subject to change as soon as new data is introduced. And it is very logical to assume so. Hence, Bayesian statistics comes extremely handy in such situations when these events are to be analyzed, along with the new evidence introduced. It is a mathematical tool that helps in the up-gradation of our belief regarding the event and the new data, which is so introduced.
Comparison of Classical Statistics and the Bayesian Statistics
Usually, when Bayesian Statistics is spoken about, a contrasting statistical inference is also always mentioned, which is the classical or the frequentist statistics and belongs to the school of thought that believes that the probabilities are merely the frequency of the events that occur in the long run, of the repeated trials. Hence when the statistical inferences are carried out, there are two approaches for the same, being the Frequentist, and Bayesian, which are therefore two very different philosophies.
What Frequentist Statistics does is that it eliminates the uncertainty by giving estimates. On the other hand, Bayesian Statistics makes an effort to accommodate the uncertainty and define it and make adjustments to the beliefs of the individuals.
Hence, a Bayesian Interpretation mainly consists of probability, which is the ‘summary of an individual’s opinion. It is also important to note that different individuals have different opinions since they have different mechanisms of interpreting the data.
In this particular framework, a person has to choose the probability of 0 when they don’t have confidence in the occurrence of the event, while 1 will be picked when the person is positive about the occurrence of the event. If the probability so given is between 0 and 1, then this opens doors for several outcomes.
To better understand Bayesian Inference, it is extremely important to carry out and understand the Bayes Rule, and then interpret it in the correct manner. The application of conditional probability can hence derive the Bayes Theorem.
Read: Bayesian Networks
Help in Understanding and Interpreting Bayes Rule for Executing the Bayesian Inference.
As stated before, the main idea of the Bayesian Inference is to upgrade any of our beliefs about the events as any new data is presented with. This is a more organic way to talk about the probable events. As more data is accumulated, the previous beliefs get washed out.
One example through which this can be explained is to consider the Moon’s prior belief is to collide with Earth. With the passage of every night, if we were to consider these events with regard to the Bayesian Inference, it will tend to modify some of our previous beliefs, that it is very less likely that the Moon will be colliding with the Earth. The belief that the Moon is likely to remain in its orbit is going to be reinforced.
Also, to follow up the concrete probability, a coin flip example of the Bayesian inference.
It is very significant to understand that the concept can be mathematically applied. A significant aspect of understanding the Bayesian Inference is the understanding of the parameters and models.
Models are the main mathematically formulated events. The parameters, therefore, are the factors of the models that affect the data. In the example of observing the fairness of the coin, it is defined as the parameter denoted by (theta). The outcome, let’s assume as A. The question that we can answer with the data at hand:
The outcome, which is given as (A), determines the probability of the coin being fair. (theta=0.5)
When we use the data to numerically represent it using the Bayes Theorem, the formula which presents itself:
P(Ф/A) = [P(A/Ф)*P(Ф)/P(A)]
P(Ф) here refers to the prior strength of our belief, which was with regard to the fairness of the coin before the toss. Here the probability of the fairness of the coin levitates between 0 and 1.
P(A/Ф) = This signifies the probability of observing the result of our distribution of theta. In simpler terms, if the coin was fair, the probability of observing the number of heads in the particular number of flips is observed.
P (Ф/D hence observes the past belief of our parameters, after taking new data into consideration, which is new heads.
For an effective definition of the models, it is important to understand that the models themselves need to be defined effectively, that too beforehand. Firstly, it shows the likelihood function, which is represented by P(D/Ф), while others show prior beliefs. The product then gives the past belief function, which is P(Ф/A).
Must Read: Naive Bayes Explained
Hence, Bayesian Statistics then exists as a framework for describing the perfect case of uncertainty, with the help of mathematics and the tool of probability. On a very simple level, the ‘classical’ outlook for performing the inference very closely looks like the Bayesian Statistical Method, which involves making a prior assumption.
In a nutshell, the frequentists take the relative chance of datasets, as compared to the Bayesian Inference. The information which is so provided here has been explained in a very basic and simple manner, which is enough for any individual to gather the fundamentals of the core concept.
If you would like to know more about careers in Machine Learning and Artificial Intelligence, check out IIT Madras and upGrad’s Advanced Certification in Machine Learning and Cloud.
What is a limitation of Bayesian statistics?
Bayesian statistics is a set of techniques for analyzing data that arise from a set of random variables. It works on the probability distribution of the parameters and can be used to make inference about parameters. It has some limitations, like the probabilistic approach is not valid for many scientific applications. Bayesian statistics is a very powerful tool in statistics that allows coming up with the best possible solution to a problem. However, it only works when there is enough data. In a case where there isn’t enough data, the Bayesian solution does not work.
What are the applications of Bayesian statistics?
Bayesian statistics is applied to fields such as medicine and engineering. In medicine, Bayesian statistics are used for medical diagnosis, especially for hard medical conditions. For example, Bayesian statistics can be used to estimate the probability of a cancer to spread to other organs based on some imaging data. Another field where Bayesian statistics are widely used is bioinformatics. Bayesian statistics are used to identify DNA sequence alignments in biological databases. Another field that relies on Bayesian statistics is engineering. In the field of engineering, Bayesian statistics are utilized in reliability analysis, software testing, and many other engineering analyses.
What is the major advantage of Bayesian inference?
One major advantage of Bayesian inference is that, unlike other forms of inference, the role of data is explicitly represented. While the results of an experiment can never prove a theory correct and can also never prove it incorrect, Bayesian inference can nevertheless make statements about the probability that a given theory is correct. This is an advantage over many other forms of inference in which the data is either ignored or given no role.