In Bayesian analysis, the prior distribution—a probability distribution for the unknown parameters—encodes the expert scientific judgment. Nowadays, PhD research is being done with the help of Bayesian methods to make the process easier. But what is the Bayesian method and how is it applied to make PhD research easier? Here, we will identify the applications of Bayesian methods in PhD research in this blog. First, we will define the Bayesian method and its application in PhD research.
The reason the Bayesian method originated is because of the introduction of frequentist statistics. Frequentist statistics determine whether a hypothesis is true or false. In the long run of the experiment, it determines the likelihood of an event (i.e the experiment is repeated under the same conditions to obtain the outcome). Finally, we will conclude the blog with a summary of what we have covered.
Define Bayesian Method
The main challenge to the traditional method of statistical inference is the development of Bayesian approaches. The technique of employing data analysis to deduce characteristics of an underlying probability distribution is known as statistical inference. For instance, inferential statistical analysis infers characteristics of a population by producing estimates and testing hypotheses. A method for learning from data is Bayes' theorem. A researcher weighs their prior assumptions regarding the magnitude of an intervention's effect by the evidence obtained from experimentation using Bayes' theorem. Let me summarise with the help of an example.
What is the probability that climate change will occur in the next 10 years? You cannot answer that question by using the frequentist perspective. But, with the Bayesian approach, you can get data having high probability by learning from the previous data. A researcher weighs their prior assumptions regarding the magnitude of an intervention's effect by the evidence obtained from experimentation using Bayes' theorem.
Bayesian analysis and decision-making is a method for reaching fact-based conclusions regarding a specific hypothesis based on both previously known information pertinent to that hypothesis and fresh data that has been gathered especially to address it. Now, let us know the applications of the Bayesian approach that has helped the PhD researchers largely.
Figure 1: Bayesian method
Application of Bayesian Method in PhD research
In Bayesian decision-making, choices are made based on the probability that an outcome will be successful, where this probability is affected by both the decision-prior maker's knowledge and new information. The statistical technique used to determine these probabilities is called Bayesian analysis.
For example, the Bayesian theorem is widely accepted in medical research. It uses data from previous medical records and it determines the probability of whether a patient has a certain disease or not. The reason the Bayesian method is widely accepted in the PhD research is that it counts the factors needed to measure the outcome and eventually provides the probabilities instead of the binary results.
In order to evaluate the likelihood of a condition, posterior probabilities from Bayes' theorem are computed and linked with clinical information about diseases and symptoms. By examining historical patient data and identifying a pattern that can reveal if a patient has Alzheimer's disease, Bayesian inference is utilized to make a diagnosis. When making predictions about uncommon diseases, when occurrences may be sporadic and a lot of data is needed, Bayes' theorem is particularly helpful.
Now let us discuss some problems you can get while applying Bayesian methods. The initial knowledge of selecting a prior is also essential in applying Bayesian methods in PhD research. The previous selection is to be done in any kind of method. The ability to convert irrational prior beliefs into a mathematically specified prior is necessary for Bayesian findings. Without exercising caution, you could produce false findings. It frequently has a significant computational cost, particularly in models with numerous parameter choices. In addition, if a different random seed is used, simulations yield slightly different results. It should be noted that minor deviations in simulation results do not refute the initial assertion that Bayesian judgments are precise. Given the likelihood function and the priors, the posterior distribution of a parameter is accurate, however simulation-based estimations of the posterior numbers can vary depending on the random number generator employed in the methods. It may result in posterior distributions with strong previous influence. Practically speaking, it could occasionally be challenging to persuade subject-matter experts who disagree with the validity of the selected prior.
Figure 2: Usage of Bayesian methods
Finally, we can conclude that Bayesian methods are creating wonders in PhD research but there are some factors to look out for while applying the Bayesian methods. It uses data and probability to help the PhD researchers get accurate results but the initial knowledge of selecting a prior is needed and also this process requires a little more time than the other methods. We, at Regent Statistics, provide you with high-quality PhD dissertations at an affordable price that covers all your needs.