P(w|h): Probability of word w, given some history h
Example: P(because| today I am happy)
w: because
h: today I am happy

# Approach 1: Relative Frequency Count
Step 1: Take a text corpus
Step 2: Count the number of times 'today I am happy' appears
Step 3: Count the number of times it is followed by 'because'

P(because| today I am happy) =
Count(today I am happy because)/
Count(today I am happy)
# In essence, we seek to answer the question, 
Out of the N times we saw the history h, 
how many times did the word w follow it?


# Disadvantages of the approach:

When the size of the text corpus is large, 
then this approach has to traverse the entire corpus. 
Not scalable and is clearly suboptimal in performance.

# Approach 2: Bigram Model
Bigram model approximates the probability of a word given all the previous words by using only the conditional probability of the preceding word.