Bayesian model selection
Okay kiddo, so imagine you have a big bag of legos. Some legos might be better than others for building a certain toy. That's kind of like how Bayesian model selection works.
When we want to make predictions about something in the world, we use models. A model is like a set of instructions for how things work. For example, if we want to predict how many people will buy ice cream on a hot day, we might make a model that says "If it's very hot outside, then more people will buy ice cream."
But not all models are created equal. Some models might work better for certain predictions and others might not work as well. So, just like we would pick out certain legos from our bag to build a specific toy, we need to choose the right model for the job.
Bayesian model selection helps us choose the best model by using something called probability. Probability is like a way of measuring how likely something is to happen. Imagine if you rolled a dice, and there were six possible outcomes. Each outcome has a probability of happening - one out of six chance for each number.
If we have two different models (remember, those sets of instructions) for predicting how many people will buy ice cream, we can use probability to see which one does the best job.
To do this, we look at the data we have about how many people bought ice cream on past hot days. Then, we see which model is most likely to have generated that data. Think of it like a detective trying to figure out who committed a crime based on the evidence they found at the scene.
The model that is most likely to have generated the data is the one we choose! It's like finding the right legos we need for building the best toy. We can use this model to make predictions about how many people will buy ice cream on the next hot day.
So, Bayesian model selection helps us pick the best set of instructions (or model) for making a prediction, based on the evidence we have. And just like we need the right legos to build the best toy, we need the right model to make the best predictions.
When we want to make predictions about something in the world, we use models. A model is like a set of instructions for how things work. For example, if we want to predict how many people will buy ice cream on a hot day, we might make a model that says "If it's very hot outside, then more people will buy ice cream."
But not all models are created equal. Some models might work better for certain predictions and others might not work as well. So, just like we would pick out certain legos from our bag to build a specific toy, we need to choose the right model for the job.
Bayesian model selection helps us choose the best model by using something called probability. Probability is like a way of measuring how likely something is to happen. Imagine if you rolled a dice, and there were six possible outcomes. Each outcome has a probability of happening - one out of six chance for each number.
If we have two different models (remember, those sets of instructions) for predicting how many people will buy ice cream, we can use probability to see which one does the best job.
To do this, we look at the data we have about how many people bought ice cream on past hot days. Then, we see which model is most likely to have generated that data. Think of it like a detective trying to figure out who committed a crime based on the evidence they found at the scene.
The model that is most likely to have generated the data is the one we choose! It's like finding the right legos we need for building the best toy. We can use this model to make predictions about how many people will buy ice cream on the next hot day.
So, Bayesian model selection helps us pick the best set of instructions (or model) for making a prediction, based on the evidence we have. And just like we need the right legos to build the best toy, we need the right model to make the best predictions.