"Our mates have gone"
Pete Edochie emotionally recounting his last encounter with Olu Jacobs.
Sadly, many went too early. Earlier than they should have, if they had different circumstances.
Francesco Totti: "The best player I faced? Steven Gerrard. A Champion! An example! He would be captain of my World XI dream team."
Lionel Messi: "I think Gerrard is England's greatest ever player."
Arsene Wenger: "He was a fantastic player who had the qualities that every midfielder dreams of. He scored goals, he could pass. He had commitment and motivation."
Kaka: "For me, and I have always said this, Gerrard will be regarded as one of the greatest midfielders ever when he finishes his career. No doubt."
Thierry Henry: "I find it a disgrace that he didn't win European Footballer of the Year in 2005 after Istanbul. For me, he is one of the best ever. Whenever you play Liverpool you know you have to get him out of the game. If not, it's all over for you."
Patrick Vieira: "The best midfielder I would say is Steven Gerrard. I really rate him as a player and a man."
Wayne Rooney: "The best player I'd played for England with? I'd have to say Stevie. For me he is an incredible player, an incredible leader. He helped me a lot during my early days with England."
Paul Scholes: "Who was better - me or Gerrard? I can answer that. Gerrard. Gerrard's a great player. I wouldn't be able to do what he did at Liverpool."
Zinedine Zidane: "I have said in the past that at his peak he was the best in the world. I think it was the summer of 2004 I was having a conversation with Florentino (Perez) and I told him I wanted him to partner me in midfield for Madrid."
Andrea Pirlo: "That night in Istanbul, Liverpool's fight back was centralised on Steven Gerrard. He was their leader, their star player, their man who made it happen."
Pele in 2007: "For me, for the last five years Gerrard has been the best player in the world."
🚨 OFFICIAL: James Milner retires from professional football. He has the record for most appearances ever in Premier League.
🏆🏆🏆 Premier League
🏆🏆 FA Cup
🏆🏆 Community Shield
🏆🏆 League Cup
🏆 Champions League
In 20 years, vibe coders will look at the Linux kernel repo the way we look at the pyramids. In awe, unable to imagine how they managed to drag all those giant stones and pile them up in the middle of the desert.
I found out my girlfriend cheated on me. Instead of breaking up right away, I made a fake account, sent her the proof anonymously, and told her that if she didn’t send me money, I’d tell her boyfriend everything.
I shared this whole plan with my best friend for advice, but this mf went behind my back and shared everything with my girlfriend.
When confronted, he said
Why does it matter? I thought she deserved to know.
He wasn’t just betraying me. He was behaving like a random variable after you marginalize out all the hidden information.
In probability, to understand what you actually know, you marginalize over hidden variables.
That means you sum over all the possibilities you can’t observe to compute the probability of what you can observe.
Marginal probability is a statistical measure that represents the probability of a single event by aggregating over all possible values of other variables.
Formula
P(A) = Σ P(A, Bi)
Where
P(A) = Marginal probability of event A
P(A, Bi) = Joint probability of A and B
Σ = Summation
Let's take an example and solve step by step
A dating app wants to find the probability of users sending messages, regardless of whether they get a response. The data shows message sent vs response received:
Short forms
- M = Message
- R = Response
Joint Probability Table
- M (Yes), R (Yes) = 0.30
- M (Yes), R (No) = 0.25
- M (No), R (Yes) = 0.10
- M (No), R (No) = 0.35
Step 1 What we want to marginalize
- We want P(M = Yes)
Step 2 Joint probabilities for M = Yes
- P(M = Yes, R = Yes) = 0.30
- P(M = Yes, R = No) = 0.25
Step 3 Apply marginal probability
- P(M = Yes)
- P(M=Yes, R=Yes) + P(M=Yes, R=No)
- 0.30 + 0.25 = 0.55
P(Message = Yes) = 0.55
Final Answer
The marginal probability of a user sending a message is 0.55 or 55%, regardless of whether they receive a response.
Congratulations, you've just learned Marginal Probability.
Bonus: Applications in AI/ML
1. Bayesian Networks:
Computing marginal probabilities by summing out irrelevant variables to make predictions and inferences in graphical models.
2. Latent Variable Models:
In topic modeling (LDA) and hidden Markov models, marginalizing over hidden states to find the probability of observed data.
3. Feature Selection:
Identifying which features independently correlate with target variables by computing marginal distributions, helping reduce dimensionality.
4. Probabilistic Classification:
Naive Bayes classifiers use marginal probabilities of features to classify data, assuming independence between features.
🚨| Wholesome moment as Speed rewarded a talented young kid in Botswana with $5,000 after he taught him how to play the marimba 🥹❤️
Nako e e tletseng boitumelo fa Speed a neela ngwana yo o nang le talente kwa Botswana $5,000 morago ga gore a mo ruthe go tshameka marimba 🥹❤️
KAN U BELIEVE IT 🙏 whether you call am kanu Abi na canoe, wetin I know be say, my man de flow 😂 kanu nwankwo anyi ga enwemere and today anyi enwemere we won 💪👏 congratulations to our dear super eagles @ng_supereagles well done we move to next game we are praying 🙏
Even if we disassembled the rocky planets, converted them into fusion reactors, and fueled them with the gas giants — we still couldn't hold a candle to the Sun.