This article was published in Scientific American’s former blog network and reflects the views of the author, not necessarily those of Scientific American I’m not sure when I first heard of Bayes’ ...

When two people with strong prior beliefs at opposite ends of the spectrum meet, there is absolutely no way either of them can influence the other. Now think about those prime time discussions on news ...

ベイズの定理はなぜ必要なのか？ ベイズの定理は、不確実性の下での意思決定を支援する強力な統計ツールです。この理論は、過去のデータや経験に基づいて未来の出来事の確率を推定するのに役立ちます。特に、データサイエンス、機械学習、経済分析 ...

前回は、グリッド・サーチと交差検証を紹介しました。今回は、ナイーブ・ベイズ（Naive Bayes）の解説をします。単純ベイズとも呼ばれるモデルです。 割と直感的に理解しやすいアルゴリズムですが、ベイズ定理という確率論に基づいた分類器です。

We live in a world where a lot of things seem to happen by pure chance, from winning the Lotto to losing your car keys. But the truth is, the likelihood of many everyday things happening is heavily ...

The stock market is an ever-changing place. In fact, it’s changing every second of every day as prices go up and down, and new factors impact the trajectory of the market. It’s important for investors ...

Bayes' theorem, also called Bayes' rule or Bayesian theorem, is a mathematical formula used to determine the conditional probability of events. The theorem uses the power of statistics and probability ...

One of the most important notions in probability and statistics is Bayes' Theorem, and it can be a little difficult to understand. It involves negotiating probabilities of conditional events to find ...

Inside probability theory, conditional probability is a way to calculate and measure the probability of some event happening if another event has already occurred. The Bayes’ Theorem is one way of ...