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I think this is largely unnecessary splitting hairs.

Conditional probability $P(x\mid y)\equiv P(X=x \mid Y=y)$ of $x$ given $y$ is defined for two random variables $X$ and $Y$ taking values $x$ and $y$. But we can also talk about probability $P(x\mid\theta)$ of $x$ given $\theta$ where $\theta$ is not a random variable but a parameter.

Note that in both cases the same term "given" and the same notation $P(\cdot\mid\cdot)$ can be used. There is no need to invent different notations. Moreover, what is called "parameter" and what is called "random variable" can depend on your philosophy, but the math does not change.

The first quote from Wikipedia states that $\mathcal{L}(\theta \mid x) = P(x \mid \theta)$ by definition. Here it is assumed that $\theta$ is a parameter. The second quote says that $\mathcal{L}(\theta \mid x)$ is not a conditional probability. This means that it is not a conditional probability of $\theta$ given $x$; and indeed it cannot be, because $\theta$ is assumed to be a parameter here.

In the context of Bayes theorem $$P(a\mid b)=\frac{P(b\mid a)P(a)}{P(b)},$$ both $a$ and $b$ are random variables. But we can still call $P(b\mid a)$ "likelihood" (of $a$), and now it is also a bona fide conditional probability (of $b$). This terminology is standard in Bayesian statistics. Nobody says it is something "similar" to the likelihood; people simply call it the likelihood.

Note 1: In the last paragraph, $P(b\mid a)$ is obviously a conditional probability of $b$. As a likelihood $\mathcal L(a\mid b)$ it is seen as a function of $a$; but it is not a probability distribution (or conditional probability) of $a$! Its integral over $a$ does not necessarily equal $1$. (Whereas its integral over $b$ does.)

Note 2: Sometimes likelihood is defined up to an arbitrary proportionality constant, as emphasized by @MichaelLew (because most of the time people are interested in likelihood ratios). This can be useful, but is not always done and is not essential.

See also What is the difference between "likelihood" and "probability"? and in particular @whuber's answer there.

I fully agree with @Tim's answer in this thread too (+1).

I think this is largely unnecessary splitting hairs.

Conditional probability $P(x\mid y)\equiv P(X=x \mid Y=y)$ of $x$ given $y$ is defined for two random variables $X$ and $Y$ taking values $x$ and $y$. But we can also talk about probability $P(x\mid\theta)$ of $x$ given $\theta$ where $\theta$ is not a random variable but a parameter.

Note that in both cases the same term "given" and the same notation $P(\cdot\mid\cdot)$ can be used. There is no need to invent different notations. Moreover, what is called "parameter" and what is called "random variable" can depend on your philosophy, but the math does not change.

The first quote from Wikipedia states that $\mathcal{L}(\theta \mid x) = P(x \mid \theta)$ by definition. Here it is assumed that $\theta$ is a parameter. The second quote says that $\mathcal{L}(\theta \mid x)$ is not a conditional probability. This means that it is not a conditional probability of $\theta$ given $x$; and indeed it cannot be, because $\theta$ is assumed to be a parameter here.

In the context of Bayes theorem $$P(a\mid b)=\frac{P(b\mid a)P(a)}{P(b)},$$ both $a$ and $b$ are random variables. But we can still call $P(b\mid a)$ "likelihood" (of $a$), and now it is also a bona fide conditional probability (of $b$). This terminology is standard in Bayesian statistics. Nobody says it is something "similar" to the likelihood; people simply call it the likelihood.

Note 1: In the last paragraph, $P(b\mid a)$ is obviously a conditional probability of $b$. As a likelihood $\mathcal L(a\mid b)$ it is seen as a function of $a$; but it is not a probability distribution (or conditional probability) of $a$! Its integral over $a$ does not necessarily equal $1$. (Whereas its integral over $b$ does.)

Note 2: Sometimes likelihood is defined up to an arbitrary proportionality constant, as emphasized by @MichaelLew (because most of the time people are interested in likelihood ratios). This can be useful, but is not always done and is not essential.

See also What is the difference between "likelihood" and "probability"? and in particular @whuber's answer there.

I fully agree with @Tim's answer in this thread too (+1).

I think this is largely unnecessary splitting hairs.

Conditional probability $P(x|y)\equiv P(X=x|Y=y)$ of $x$ given $y$ is defined for two random variables $X$ and $Y$ taking values $x$ and $y$. But we can also talk about probability $P(x|\theta)$ of $x$ given $\theta$ where $\theta$ is not a random variable but a parameter.

Note that in both cases the same term "given" and the same notation $P(\cdot|\cdot)$ can be used. There is no need to invent different notations. Moreover, what is called "parameter" and what is called "random variable" can depend on your philosophy, but the math does not change.

The first quote from Wikipedia states that $\mathcal{L}(\theta |x) = P(x | \theta)$ by definition. Here it is assumed that $\theta$ is a parameter. The second quote says that $\mathcal{L}(\theta |x)$ is not a conditional probability. This means that it is not a conditional probability of $\theta$ given $x$; and indeed it cannot be, because $\theta$ is assumed to be a parameter here.

In the context of Bayes theorem $$P(a|b)=\frac{P(b|a)P(a)}{P(b)},$$ both $a$ and $b$ are random variables. But we can still call $P(b|a)$ "likelihood" (of $a$), and now it is also a bona fide conditional probability (of $b$). This terminology is standard in Bayesian statistics. Nobody says it is something "similar" to the likelihood; people simply call it the likelihood.

Note 1: In the last paragraph, $P(b|a)$ is obviously a conditional probability of $b$. As a likelihood $\mathcal L(a|b)$ it is seen as a function of $a$; but it is not a probability distribution (or conditional probability) of $a$! Its integral over $a$ does not necessarily equal $1$. (Whereas its integral over $b$ does.)

Note 2: Sometimes likelihood is defined up to an arbitrary proportionality constant, as emphasized by @MichaelLew (because most of the time people are interested in likelihood ratios). This can be useful, but is not always done and is not essential.

See also What is the difference between "likelihood" and "probability"? and in particular @whuber's answer there.

I fully agree with @Tim's answer in this thread too (+1).

I think this is largely unnecessary splitting hairs.

Conditional probability $P(x\mid y)\equiv P(X=x \mid Y=y)$ of $x$ given $y$ is defined for two random variables $X$ and $Y$ taking values $x$ and $y$. But we can also talk about probability $P(x\mid\theta)$ of $x$ given $\theta$ where $\theta$ is not a random variable but a parameter.

Note that in both cases the same term "given" and the same notation $P(\cdot\mid\cdot)$ can be used. There is no need to invent different notations. Moreover, what is called "parameter" and what is called "random variable" can depend on your philosophy, but the math does not change.

The first quote from Wikipedia states that $\mathcal{L}(\theta \mid x) = P(x \mid \theta)$ by definition. Here it is assumed that $\theta$ is a parameter. The second quote says that $\mathcal{L}(\theta \mid x)$ is not a conditional probability. This means that it is not a conditional probability of $\theta$ given $x$; and indeed it cannot be, because $\theta$ is assumed to be a parameter here.

In the context of Bayes theorem $$P(a\mid b)=\frac{P(b\mid a)P(a)}{P(b)},$$ both $a$ and $b$ are random variables. But we can still call $P(b\mid a)$ "likelihood" (of $a$), and now it is also a bona fide conditional probability (of $b$). This terminology is standard in Bayesian statistics. Nobody says it is something "similar" to the likelihood; people simply call it the likelihood.

Note 1: In the last paragraph, $P(b\mid a)$ is obviously a conditional probability of $b$. As a likelihood $\mathcal L(a\mid b)$ it is seen as a function of $a$; but it is not a probability distribution (or conditional probability) of $a$! Its integral over $a$ does not necessarily equal $1$. (Whereas its integral over $b$ does.)

Note 2: Sometimes likelihood is defined up to an arbitrary proportionality constant, as emphasized by @MichaelLew (because most of the time people are interested in likelihood ratios). This can be useful, but is not always done and is not essential.

See also What is the difference between "likelihood" and "probability"? and in particular @whuber's answer there.

I fully agree with @Tim's answer in this thread too (+1).

I think this is largely unnecessary splitting hairs.

Conditional probability $P(x|y)\equiv P(X=x|Y=y)$ of $x$ given $y$ is defined for two random variables $X$ and $Y$ taking values $x$ and $y$. But we can also talk about probability $P(x|\theta)$ of $x$ given $\theta$ where $\theta$ is not a random variable but a parameter.

Note that in both cases the same term "given" and the same notation $P(\cdot|\cdot)$ can be used. There is no need to invent different notations. Moreover, what is called "parameter" and what is called "random variable" can depend on your philosophy, but the math does not change.

The first quote from Wikipedia states that $\mathcal{L}(\theta |x) = P(x | \theta)$ by definition. Here it is assumed that $\theta$ is a parameter. The second quote says that $\mathcal{L}(\theta |x)$ is not a conditional probability. This means that it is not a conditional probability of $\theta$ given $x$; and indeed it cannot be, because $\theta$ is assumed to be a parameter here.

In the context of Bayes theorem $$P(a|b)=\frac{P(b|a)P(a)}{P(b)},$$ both $a$ and $b$ are random variables. But we can still call $P(b|a)$ "likelihood", and now it is also bona fide conditional probability. This terminology is standard in Bayesian statistics. Nobody says it is something "similar" to the likelihood; people simply call it the likelihood.

Note 1: In the last paragraph, $P(b|a)$ is obviously a conditional probability over $b$. As a likelihood $\mathcal L(a|b)$ it is seen as a function of $a$; but it is not a probability distribution over (or conditional probability of) $a$! Its integral over $a$ does not necessarily equal $1$. (Its integral over $b$ does.)

Note 2: Sometimes likelihood is defined up to an arbitrary proportionality constant, as emphasized by @MichaelLew (because most of the time people are interested in likelihood ratios). This can be useful, but is not always done and is not essential.

See also What is the difference between "likelihood" and "probability"? and in particular @whuber's answer there.

I fully agree with @Tim's answer in this thread too (+1).

I think this is largely unnecessary splitting hairs.

Conditional probability $P(x|y)\equiv P(X=x|Y=y)$ of $x$ given $y$ is defined for two random variables $X$ and $Y$ taking values $x$ and $y$. But we can also talk about probability $P(x|\theta)$ of $x$ given $\theta$ where $\theta$ is not a random variable but a parameter.

Note that in both cases the same term "given" and the same notation $P(\cdot|\cdot)$ can be used. There is no need to invent different notations. Moreover, what is called "parameter" and what is called "random variable" can depend on your philosophy, but the math does not change.

The first quote from Wikipedia states that $\mathcal{L}(\theta |x) = P(x | \theta)$ by definition. Here it is assumed that $\theta$ is a parameter. The second quote says that $\mathcal{L}(\theta |x)$ is not a conditional probability. This means that it is not a conditional probability of $\theta$ given $x$; and indeed it cannot be, because $\theta$ is assumed to be a parameter here.

In the context of Bayes theorem $$P(a|b)=\frac{P(b|a)P(a)}{P(b)},$$ both $a$ and $b$ are random variables. But we can still call $P(b|a)$ "likelihood" (of $a$), and now it is also a bona fide conditional probability (of $b$). This terminology is standard in Bayesian statistics. Nobody says it is something "similar" to the likelihood; people simply call it the likelihood.

Note 1: In the last paragraph, $P(b|a)$ is obviously a conditional probability of $b$. As a likelihood $\mathcal L(a|b)$ it is seen as a function of $a$; but it is not a probability distribution (or conditional probability) of $a$! Its integral over $a$ does not necessarily equal $1$. (Whereas its integral over $b$ does.)

Note 2: Sometimes likelihood is defined up to an arbitrary proportionality constant, as emphasized by @MichaelLew (because most of the time people are interested in likelihood ratios). This can be useful, but is not always done and is not essential.

See also What is the difference between "likelihood" and "probability"? and in particular @whuber's answer there.

I fully agree with @Tim's answer in this thread too (+1).