90 lines
3.3 KiB
Markdown
90 lines
3.3 KiB
Markdown
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Find a beta distribution that fits your desired confidence interval
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===================================================================
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Here is a tool for finding a beta distribution that fits your desired confidence interval.
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E.g., to find a beta distribution whose 95% confidence interval is 0.2 to 0.8,
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input 0.2, 0.8, and 0.95 in their respective fields below:
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<form action="/signup" method="post" id="fit-beta">
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<div class="field">
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<label for="ci_lower">Value at the lower end of your confidence interval</label>
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<input type="text" id="ci_lower" ci_lower="ci_lower" placeholder="0.2 to ..." class="subscribe-input"/>
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</div>
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<br>
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<div class="field">
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<label for="ci_upper">Value at the upper end of your confidence interval</label>
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<input type="text" id="ci_upper" ci_upper="ci_upper" placeholder="... to 0.8" class="subscribe-input"/>
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</div>
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<br>
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<div class="field">
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<label for="ci_length">Length of your confidence interval (0.9 = 90% by default)</label>
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<input type="text" id="ci_length" ci_length="ci_length" placeholder="0.9" class="subscribe-input"/>
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</div>
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<br>
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<button type="submit" id="submit_button"class="subscribe-button">Calculate</button>
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</form>
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<p id="result"></p>
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<script>
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const form = document.getElementById('fit-beta');
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const submit_button = document.getElementById('submit_button')
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const result_p = document.getElementById('result')
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form.addEventListener('submit', (event) => {
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event.preventDefault();
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result_p.innerHTML = ''
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// Disable new submits
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submit_button.disabled = 'disabled';
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// console.log(submit_button.innerHTML)
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submit_button.innerHTML = 'Calculating...'
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// get the form data
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let data = {
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ci_lower: form.elements['ci_lower'].value,
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ci_upper: form.elements['ci_upper'].value,
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ci_length: form.elements['ci_length'].value,
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}
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console.log(JSON.stringify(data))
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// make request
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fetch("https://trastos.nunosempere.com/fit-beta", {
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method: "post",
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headers: {
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'Accept': 'application/json',
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'Content-Type': 'application/json'
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},
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body: JSON.stringify(data)
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})
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.then( (response) => response.json())
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.then(result => {
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submit_button.disabled = false;
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submit_button.innerHTML = 'Calculate'
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result_p.innerHTML = `Result: beta(${result[0]}, ${result[1]})`
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console.log(result)
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console.log(JSON.stringify(result, null, 2))
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});
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});
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</script>
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<p>The virtue of using a a beta distribution is that it is inherently <em>bounded</em> between 0 and 1,
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and thus much more suitable for estimating things like probabilities, and, more speculatively,
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ranges of values. Previously, various people in my extended social circle, including myself, had been
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using lognormals (bad, because not bounded) or truncated lognormals (better, but deeply inelegant) for
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those purposes. I hope that with this tool such practices will come to and end.</p>
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You can read more about how this utility works [here](https://github.com/quantified-uncertainty/fit-beta.git).
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Advanced users will particularly enjoy the [npm package](https://www.npmjs.com/package/fit-beta).
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<p><img src="https://i.imgur.com/3uQgbcw.png" style="width: 20%;">
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<br>
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This is a project of the <a href="https://quantifieduncertainty.org/">Quantified Uncertainty Research Institute</a>.</p>
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<p>
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<section id='isso-thread'>
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<noscript>Javascript needs to be activated to view comments.</noscript>
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</section>
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</p>
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