In bayesian estimate we have some knowledge about the data/problem (prior).There may be several values of the parameters which explain data and hence wecan look for multiple parameters like 5 gammas and 5 lambdas that do this. As aresult of Bayesian Estimate, we get multiple models for making multiplepredcitions i.e. one for each pair of parameters but with the same prior. So, if anew example need to be predicted than computing the weighted sum of thesepredictions serves the purpose.
Maximum likelihood does not take prior into consideration (ignores the prior) soit is like being a Bayesian while using some kind of a flat prior.

When the observations in a dataset are spread equally across the range ofdistribution, then it is referred to as uniform distribution. There are no clearperks in an uniform distribution. Distributions that have more observations onone side of the graph than the other are referred to as skeweddistribution.Distributions with fewer observations on the left ( towards lowervalues) are said to be skewed left and distributions with fewer observation on theright ( towards higher values) are said to be skewed right.

Seasonality in time series occurs when time series shows a repeated pattern overtime. E.g., stationary sales decreases during holiday season, air conditioner salesincreases during the summers etc. are few examples of seasonality in a timeseries.
Seasonality makes your time series non-stationary because average value of thevariables at different time periods. Differentiating a time series is generallyknown as the best method of removing seasonality from a time series. Seasonaldifferencing can be defined as a numerical difference between a particular valueand a value with a periodic lag (i.e. 12, if monthly seasonality is present)

Sensitivity is commonly used to validate the accuracy of a classifier (Logistic,SVM, RF etc.). Sensitivity is nothing but “Predicted TRUE events/ Totalevents”. True events here are the events which were true and model alsopredicted them as true.
Calculation of senstivity is pretty straight forward-
Senstivity = True Positives /Positives in Actual Dependent Variable
Where, True positives are Positive events which are correctly classified asPositives.

Selection Bias occurs when there is no appropriate randomization acheivedwhile selecting individuals, groups or data to be analysed.Selection bias impliesthat the obtained sample does not exactly represent the population that wasactually intended to be analyzed.Selection bias consists of Sampling Bias, Data,Attribute and Time Interval.