Some kind of pile-up in the Grand National would leave the way clear for an ungenuine plodder to come from behind, avoiding all the carnage. Foinavon fits the bill and looks huge at 200/1. No bet if the others don't fall over. #conditionalprobability

I played around with the math for $GEOD a bit back in May. I’m not going to share the details of how I calculated it, and don’t take it seriously anyway. I’m just posting it because it’s fun. But I calculated the potential price of a single GEOD token to be somewhere between $25.6 USD - $7.5 million 🤣 #conditionalprobability PS: I’m not really into math, but if money’s involved, I become Laplace.

The babies born of mothers with Hep B actually are dumbasses. #ILoveMath #ConditionalProbability

Conditional probability is a crucial concept in statistics, as it allows us to incorporate prior information when making predictions. Without considering this, predictive models would lack accuracy and relevance. Understanding conditional probability is essential for creating effective models. Thank you to @TivadarDanka for sharing this insightful post! #Statistics #DataScience #PredictiveModeling #ConditionalProbability

💊 Most machine learning research is about going from mathematical modeling to ML model implementation. Here’s how to go from conditional probability to a neural architecture. Let's start by defining a simple conditional probability problem. Consider a supervised learning task where we have input data X and target data Y, and we want to model the conditional probability P(Y | X), meaning the probability of Y given X. A common way to model this in machine learning is to assume that this probability follows some parametric form and then use the data to estimate the parameters of this model. For instance, we could assume that P(Y | X) is a Gaussian distribution with mean µ(X) and standard deviation σ(X). This mean µ(X) and standard deviation σ(X) could be any functions of X, but in order to learn them from data, we often assume they can be parameterized with some parameters θ, and are differentiable with respect to these parameters. This is where neural networks come in. A neural network is just a function approximator that's highly flexible and differentiable, making it suitable to represent these functions µ(X) and σ(X). Let's assume that our neural network is a simple feed-forward network with parameters θ. Then we can write our model as: µ(X; θ) = NN_µ(X; θ) σ(X; θ) = NN_σ(X; θ) P(Y | X; θ) = N(Y; NN_µ(X; θ), NN_σ(X; θ)^2) Here, NN_µ and NN_σ are two neural networks which take the same input X and share the same parameters θ, and N is the Gaussian distribution. Their outputs represent the mean and standard deviation of the Gaussian distribution of Y given X. To train this model, we would use a method called maximum likelihood estimation (MLE), which aims to find the parameters θ that maximize the likelihood of the observed data. For our Gaussian model, this corresponds to minimizing the mean squared error between Y and NN_µ(X; θ). Below, you can see how we might implement this in code using PyTorch. In this code, we have a neural network that outputs two values for each input: a mean and a standard deviation. The loss function is defined as the negative log-likelihood of the Gaussian distribution, which we try to minimize using gradient descent. 💡 Get technical insights just like this to help you become a better ML practitioner here: lnkd.in/dSgjuK5Z #machinelearning #conditionalprobability

Naive Bayes calculates the conditional probability of each class given the input features using Bayes' theorem. kuisk.com/discipline/naive-b… via @my_twitter_username #naivebayes #bayesian #conditionalprobability #artificialintelligence #intelligence #artificialgeneralintelligence

🚨POLL: Will US equities get the popular Santa Rally ? Vote👇🏽 #Quad4 #datamining #curvefitting #conditionalprobability #setup #volatility #stocks $TSLA $AAPL $NVDA $LRCX $MU $MSFT $Vz $ABBV $VIX $MRK $RL $ULTA $LULU

Ta 50/50 och fundera på om de svaga åren har något gemensamt med 2022. #ConditionalProbability

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Let’s Understand Bayes’ Theorem in Details: python.plainenglish.io/lets-… #BayesTheorem #Statistics #DataScience #ConditionalProbability #MarginalProbability

Let’s Understand Bayes’ Theorem in Details: python.plainenglish.io/lets-… #BayesTheorem #Statistics #DataScience #ConditionalProbability #MarginalProbability

Let’s Understand Bayes’ Theorem in Details: python.plainenglish.io/lets-… #BayesTheorem #Statistics #DataScience #ConditionalProbability #MarginalProbability

Because of the way the MSM report it. I’m still not sure whether they do t understand, or are deliberately mis-stating it. But MSM says ‘you are less likely to be hospitalised with Omicron than with Delta’. What they mean is IF you have Omicron then…. #conditionalprobability

A more accurate analogy would have been "the best way to know if a person is going to have a broken leg is if they have had multiple broken legs in the past" (Yes I am THAT pedant! Also fun at parties) #AECOPD #ConditionalProbability

Me to wife: I'll be late today from work. Kindly don't keep food for me Wife: There is pizza for dinner today Me: Oh, ill eat at home #BayesTheorem explained #ConditionalProbability

great visualization of this model #datavisualization #math #conditionalprobability nytimes.com/interactive/2021…

Dear student learn ✍️conditional probability with @TAPL_Official Prepare yourself for the upcoming JEE exam . . Call : 9310378303 Visit : takshilainstitute.in #conditionalprobability #jeecoachingclass #jeecourse #bestcoachingforJEE #jeecoachingcenter #coachingindelhi

Watching Howie’s video, it’s just occurred to me that some of my students might understand conditional probability better if I highlighted the similarities with rates. #MakesNoteToSelf #Maths #ConditionalProbability

via @NYTimes #SIDS #sallyclark #conditionalprobability #prosecutorsfallacy #counterfactual nytimes.com/2021/03/08/world…