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Importance of maximum likelihood estimation
The maximum likelihood estimation is a statistical technique used to determine the values of different parameters in a model. The values of the parameters are calculated in such a way that they maximize the probability that the process performed by the model produces the desired results. This definition may still sound cryptic to students who are new to this concept, so our data analysis homework help experts have discussed it comprehensively below to make it a little easier to understand.
What are the parameters?
Often in data analysis, we use statistical models to define the process that results in the outcomes of the data being observed. For instance, we may apply a random forest model to find out whether customers will cancel a certain subscription or not. We may also apply linear models to instances where we want to determine how much revenue a company will generate based on the expenses on advertising. In these two scenarios, we can use churn modeling and linear regression modeling respectively. Each modeling technique contains specific parameters that define how the model looks like and what type of data is used to make the analysis. A linear model, for example, can be represented as y = mx + c. In this example, y could be used to represent the revenue generated by the company and x could be used to represent what is spent on advertising. The parameters for this model would be m and c. If plotted on a graph, the values for these parameters would show different lines depending on the change in values over a given period of time. From this example, it can be stated that the parameters define the blueprint of any statistical model in data analysis. To further understand the different types of parameters used in data analysis, get in touch with our maximum likelihood estimation homework help professionals.
How maximum likelihood estimation works
The maximum likelihood estimation is used to analyze data using point estimators. Point estimators are functions that are used to estimate the values of a parameter. They are mostly applied to instances where parameters cannot be found reasonably. For example, if you want to find out how much income a person earns in the US, collecting the annual income data of every citizen would be impractical. An estimate of the average salary could be more pragmatic. This is where the maximum likelihood estimation comes in. Learn more about the way this maximum likelihood estimation works which is taught by our data analysis homework helpers.
How our experts apply maximum likelihood estimation
There are four major steps used to apply maximum likelihood estimation. These are the same steps followed by our experts when providing maximum likelihood estimation homework help. Read on!
1. Defining the likelihood
Defining the likelihood ensures that you are using the right distributions for your classification or regression models.
2. Applying functions
After defining the likelihood, the natural log should be taken and the product function reduced to a sum function.
3. Maximizing or minimizing functions
Maximize or minimize the negative values of the objective function.
4. Verifying priors
Verify that uniform priors would be a safe assumption. The topic of maximum likelihood estimation has been challenging students for many years, compelling a majority of them to seek professional assistance. Good news? Students can now use our data analysis homework help platform for academics related assistance on this topic.