Bayes Updater
Start with a prior probability. Add evidence one item at a time. Watch your belief update at every step.
ADVANCED (WORTH LEARNING)
How to change your mind correctly when new info arrives
What is this?
Bayesian updating = updating your beliefs as new evidence comes in, mathematically. Most people either ignore new info (too stubborn) or overreact to it (panic). Bayes tells you exactly how much to shift your position.
You start with a prior belief (what you thought before). New evidence arrives. You update to a posterior belief (what you should think now). This tool runs that math so you don't over- or under-react to breaking news.
Real-World Example
→ News Breaks
You believed "Fed cuts in March" was 40% likely. Then a surprise inflation print comes in hot (bad for cuts). Historically, hot CPI cuts the probability of a near-term rate cut by about 35%.
Bayes Updater outputs: your new probability should be roughly 26%, not 40. The market is still at 38¢. That's now a NO bet, and you have the math to back it up.
✅Action: When news breaks, don't just react — calculate. Enter your prior, the new info, and get your updated probability instantly.
Bottom line: This is how CIA analysts think. Separate from emotion. Update systematically. Win more.
Full guide →Quick Answer
the Bayes Updater revises a probability estimate as you add evidence one piece at a time, showing how your belief shifts at each step. You start from a prior, then update by how much more likely the evidence is if your hypothesis is true versus false — the mathematically correct way to change your mind.
Worked Example
Interactive Calculator
Your starting probability before adding evidence
Probability Chain
Use the Bayes Updater inside your AI agent
This tool is also live on our MCP server as bayes_update. Connect Claude, ChatGPT, Cursor, or Copilot and call it directly — six core calculators are free.
Go Pro · 26% off your first year
Every edge the engines find, the moment they find it
Pro opens every engine on the site — the cross-market arb scanner, the Mispricing Scanner, Thee Oracle, and the silver, gold, oil and bitcoin edge models that price commodity contracts in real time.
$111 for the first year, then $150/yr. Code applies automatically. Ends Dec 31. Monthly is $14.99/mo.
Every signal graded in public, losers left on the board. See the record →
Related Tools
What is Bayesian Updating?
Bayesian updating is the mathematically correct way to revise a probability when new evidence arrives. You start with a prior — your best estimate before seeing any evidence — and update it each time you learn something new. The result is a posterior probability that accounts for both what you believed before and how strongly the new evidence should shift that belief.
In prediction markets, this is how sharp traders think. The market prices a Fed rate cut at 45%. New jobs data comes in stronger than expected. How much should that shift your estimate? It depends on two things: how likely was that jobs report if the Fed is going to cut, and how likely was it if they're not? The Bayes formula takes both answers and gives you the right posterior.
Likelihood Ratios
The two inputs for each piece of evidence are its likelihoods: how probable was this evidence given that the hypothesis is true, versus given that it's false? If strong jobs data is just as likely whether the Fed cuts or not, it shouldn't move your estimate much. But if strong jobs data is far more likely when the Fed holds rates, it should push your estimate down significantly. The ratio of those two likelihoods is what drives the update.
Chaining updates
The power of this tool is chaining multiple evidence items. Each update feeds into the next — the posterior from step 1 becomes the prior for step 2. This is exactly how information should accumulate over time, and it's why prediction markets tend to be more efficient as more evidence arrives and traders update their beliefs in sequence.
Frequently Asked Questions
What is Bayesian updating in prediction markets?
Bayesian updating is the mathematically correct way to revise a probability estimate when new evidence arrives. You start with a prior probability, then update it based on how likely the evidence would be if the hypothesis were true versus false. The result is a posterior probability that correctly weights both your prior belief and the new information.
How do I use the Bayes Updater tool on Kalshi markets?
Start by entering your prior probability — your best estimate before any new evidence. Then add evidence items one at a time: for each piece of evidence, set how likely that evidence would be if the event resolves YES versus NO. The tool updates your probability at each step and shows you the full chain.
When should I use Bayesian updating vs checking the base rate?
Use the Base Rate Scanner to set your prior — it anchors your starting probability to historical frequency. Then use the Bayes Updater to revise that prior as event-specific evidence arrives. The two tools work together: base rate gives you the starting point, Bayesian updating moves you from there.
Get The 7 Oracles' daily edge — subscribe free
No spam. Unsubscribe anytime.