Merge pull request #96 from probcomp/20260311-fsaad-links

Fix #88: Links to documentation are broken in the tutorials

Author: ztangent

tutorials/A Bottom-Up Introduction to Gen.ipynb

@@ -2170,7 +2170,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The ability to *trace* the values of random choices in a probabilistic program (i.e. record the value of each choice in a trace data structure) is one of the basic features of Gen's built-in modeling language. To write a function in this language we use the `@gen` macro provided by Gen. Note that the built-in modeling language is just one way of defining a [generative function](https://www.gen.dev/docs/stable/ref/distributions/).\n",
+    "The ability to *trace* the values of random choices in a probabilistic program (i.e. record the value of each choice in a trace data structure) is one of the basic features of Gen's built-in modeling language. To write a function in this language we use the `@gen` macro provided by Gen. Note that the built-in modeling language is just one way of defining a [generative function](https://www.gen.dev/docs/stable/ref/modeling/distributions/#distributions).\n",
     "\n",
     "Below, we write a `@gen function` version of the function `f` defined above, this time using Gen's tracing instead of our own:"
    ]
@@ -2225,7 +2225,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "To run a `@gen` function and get a trace of the execution, we use the [`simulate`](https://www.gen.dev/docs/stable/ref/gfi/#Gen.simulate) method:"
+    "To run a `@gen` function and get a trace of the execution, we use the [`simulate`](https://www.gen.dev/docs/stable/ref/core/gfi/#Gen.simulate) method:"
    ]
   },
   {
@@ -2715,7 +2715,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We can also get the log probability that an individual trace would be generated by the function ($\\log p(t; x)$), using the [`get_score`](https://www.gen.dev/docs/stable/ref/gfi/#Gen.get_score) method.\n",
+    "We can also get the log probability that an individual trace would be generated by the function ($\\log p(t; x)$), using the [`get_score`](https://www.gen.dev/docs/stable/ref/core/gfi/#Gen.get_score) method.\n",
     "\n",
     "Let's generate a trace below, get its log probability with `get_score`"
    ]
@@ -2778,7 +2778,7 @@
     "        gen_f(0.3)\n",
     "    ```\n",
     "\n",
-    "2. Using the [`simulate`](https://www.gen.dev/docs/stable/ref/gfi/#Gen.simulate) method:\n",
+    "2. Using the [`simulate`](https://www.gen.dev/docs/stable/ref/core/gfi/#Gen.simulate) method:\n",
     "\n",
     "    ```julia\n",
     "        trace = simulate(gen_f, (0.3,))\n",
@@ -2789,7 +2789,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We can also generate a trace that satisfies a set of constraints on the valus of random choices using the [`generate`](https://www.gen.dev/docs/stable/ref/gfi/#Gen.generate) method. Suppose that we want a trace where `:a` is always `true` and `:c` is always `false`. We first construct a choice map containing these constraints:"
+    "We can also generate a trace that satisfies a set of constraints on the valus of random choices using the [`generate`](https://www.gen.dev/docs/stable/ref/core/gfi/#Gen.generate) method. Suppose that we want a trace where `:a` is always `true` and `:c` is always `false`. We first construct a choice map containing these constraints:"
    ]
   },
   {
@@ -3013,7 +3013,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "A more efficient and numerically robust implementation of importance resampling is provided in Gen's inference library (see [`importance_resampling`](https://www.gen.dev/docs/stable/ref/inference/#Gen.importance_resampling))."
+    "A more efficient and numerically robust implementation of importance resampling is provided in Gen's inference library (see [`importance_resampling`](https://www.gen.dev/docs/stable/ref/inference/importance/#Gen.importance_sampling)."
    ]
   },
   {
@@ -3433,7 +3433,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Now, we use the [`update`](https://www.gen.dev/docs/stable/ref/gfi/#Gen.update) method, to change the value of `:c` from `true` to `false`:"
+    "Now, we use the [`update`](https://www.gen.dev/docs/stable/ref/core/gfi/#Gen.update) method, to change the value of `:c` from `true` to `false`:"
    ]
   },
   {

tutorials/A Bottom-Up Introduction to Gen.jl

@@ -188,7 +188,7 @@ plot(map(p -> query(p, 14), [0.1, 0.5, 0.9])...)
 
 # ## 2. Tracing the values of random choices in generative functions
 
-# The ability to *trace* the values of random choices in a probabilistic program (i.e. record the value of each choice in a trace data structure) is one of the basic features of Gen's built-in modeling language. To write a function in this language we use the `@gen` macro provided by Gen. Note that the built-in modeling language is just one way of defining a [generative function](https://www.gen.dev/docs/stable/ref/distributions/).
+# The ability to *trace* the values of random choices in a probabilistic program (i.e. record the value of each choice in a trace data structure) is one of the basic features of Gen's built-in modeling language. To write a function in this language we use the `@gen` macro provided by Gen. Note that the built-in modeling language is just one way of defining a [generative function](https://www.gen.dev/docs/stable/ref/modeling/distributions/#distributions).
 #
 # Below, we write a `@gen function` version of the function `f` defined above, this time using Gen's tracing instead of our own:
 
@@ -210,7 +210,7 @@ end;
 
 gen_f(0.3)
 
-# To run a `@gen` function and get a trace of the execution, we use the [`simulate`](https://www.gen.dev/docs/stable/ref/gfi/#Gen.simulate) method:
+# To run a `@gen` function and get a trace of the execution, we use the [`simulate`](https://www.gen.dev/docs/stable/ref/core/gfi/#Gen.simulate) method:
 
 using Gen: simulate
 trace = simulate(gen_f, (0.3,));
@@ -300,7 +300,7 @@ for prob_a in [0.1, 0.5, 0.9]
     println("expected: $(prob_true(prob_a)), actual: $actual")
 end
 
-# We can also get the log probability that an individual trace would be generated by the function ($\log p(t; x)$), using the [`get_score`](https://www.gen.dev/docs/stable/ref/gfi/#Gen.get_score) method.
+# We can also get the log probability that an individual trace would be generated by the function ($\log p(t; x)$), using the [`get_score`](https://www.gen.dev/docs/stable/ref/core/gfi/#Gen.get_score) method.
 #
 # Let's generate a trace below, get its log probability with `get_score`
 
@@ -321,13 +321,13 @@ println("log probability: $(get_score(trace))")
 #         gen_f(0.3)
 #     ```
 #
-# 2. Using the [`simulate`](https://www.gen.dev/docs/stable/ref/gfi/#Gen.simulate) method:
+# 2. Using the [`simulate`](https://www.gen.dev/docs/stable/ref/core/gfi/#Gen.simulate) method:
 #
 #     ```julia
 #         trace = simulate(gen_f, (0.3,))
 #     ```
 
-# We can also generate a trace that satisfies a set of constraints on the valus of random choices using the [`generate`](https://www.gen.dev/docs/stable/ref/gfi/#Gen.generate) method. Suppose that we want a trace where `:a` is always `true` and `:c` is always `false`. We first construct a choice map containing these constraints:
+# We can also generate a trace that satisfies a set of constraints on the valus of random choices using the [`generate`](https://www.gen.dev/docs/stable/ref/core/gfi/#Gen.generate) method. Suppose that we want a trace where `:a` is always `true` and `:c` is always `false`. We first construct a choice map containing these constraints:
 
 # +
 using Gen: choicemap
@@ -414,7 +414,7 @@ function my_importance_sampler(gen_fn, args, constraints, num_traces)
     return traces[idx]
 end;
 
-# A more efficient and numerically robust implementation of importance resampling is provided in Gen's inference library (see [`importance_resampling`](https://www.gen.dev/docs/stable/ref/inference/#Gen.importance_resampling)).
+# A more efficient and numerically robust implementation of importance resampling is provided in Gen's inference library (see [`importance_resampling`](https://www.gen.dev/docs/stable/ref/inference/importance/#Gen.importance_sampling).
 
 # Suppose our goal is to sample `:a` and `:b` from the conditional distribution given that we have observed `:c` is `false`. That is, we want to sample choice map $t$ with probability $0$ if $t(c) = \mbox{false}$ and otherwise probability:
 #
@@ -473,7 +473,7 @@ plot(map(N -> importance_query(0.3, N), [1, 10, 100])...)
 (trace, weight) = generate(foo, (0.3,), choicemap((:a, true), (:b, true), (:c, true)));
 get_choices(trace)
 
-# Now, we use the [`update`](https://www.gen.dev/docs/stable/ref/gfi/#Gen.update) method, to change the value of `:c` from `true` to `false`:
+# Now, we use the [`update`](https://www.gen.dev/docs/stable/ref/core/gfi/#Gen.update) method, to change the value of `:c` from `true` to `false`:
 
 # +
 using Gen: update, NoChange

tutorials/Data-Driven Proposals in Gen.ipynb

@@ -909,7 +909,7 @@
     "\n",
     "To see how to use the built-in importance resampling function, run\n",
     "```?Gen.importance_resampling``` or check out the\n",
-    "[documentation](https://www.gen.dev/docs/dev/ref/importance/#Gen.importance_resampling)."
+    "[documentation](https://www.gen.dev/docs/stable/ref/inference/importance/#Gen.importance_resampling)."
    ]
   },
   {
@@ -1213,7 +1213,7 @@
    "metadata": {},
    "source": [
     "The inference algorithm above used a variant of\n",
-    "[`Gen.importance_resampling`](https://www.gen.dev/docs/stable/ref/importance/#Gen.importance_resampling)\n",
+    "[`Gen.importance_resampling`](https://www.gen.dev/docs/stable/ref/inference/importance/#Gen.importance_sampling)\n",
     "that does not take a custom proposal distribution. It uses the default\n",
     "proposal distribution associated with the generative model. For generative\n",
     "functions defined using the built-in modeling DSL, the default proposal\n",
@@ -1319,7 +1319,7 @@
    "metadata": {},
    "source": [
     "We will propose the x-coordinate of the destination from a\n",
-    "[piecewise_uniform](https://www.gen.dev/docs/dev/ref/distributions/#Gen.piecewise_uniform)\n",
+    "[piecewise_uniform](https://www.gen.dev/docs/stable/ref/modeling/distributions/#Gen.piecewise_uniform)\n",
     "distribution, where we set higher probability for certain bins based on the\n",
     "heuristic described above and use a uniform continuous distribution for the\n",
     "coordinate within a bin. The `compute_bin_probs` function below computes the\n",
@@ -1437,7 +1437,7 @@
    "metadata": {},
    "source": [
     "We can propose values of random choices from the proposal function using\n",
-    "[`Gen.propose`](https://www.gen.dev/docs/stable/ref/gfi/#Gen.propose).\n",
+    "[`Gen.propose`](https://www.gen.dev/docs/stable/ref/core/gfi/#Gen.propose).\n",
     "This method returns the choices, as well as some other information, which we\n",
     "won't need for our purposes. For now, you can think of `Gen.propose` as\n",
     "similar to `Gen.generate` except that it does not produce a full execution\n",
@@ -1568,7 +1568,7 @@
    "source": [
     "We now use our data-driven proposal within an inference algorithm. There is a\n",
     "second variant of\n",
-    "[`Gen.importance_resampling`](https://www.gen.dev/docs/stable/ref/importance/#Gen.importance_resampling)\n",
+    "[`Gen.importance_resampling`](https://www.gen.dev/docs/stable/ref/inference/importance/#Gen.importance_resampling)\n",
     "that accepts a generative function representing a custom proposal. This\n",
     "proposal generative function makes traced random choices at the addresses of\n",
     "a subset of the unobserved random choices made by the generative model. In\n",
@@ -1593,7 +1593,7 @@
     "\n",
     "This time, use only 5 importance samples (`amt_computation`). You can run\n",
     "`?Gen.importance_resampling` or check out the\n",
-    "[documentation](https://www.gen.dev/docs/stable/ref/inference/#Importance-Sampling-1)\n",
+    "[documentation](https://www.gen.dev/docs/stable/ref/inference/importance/#Gen.importance_sampling)\n",
     "to understand how to supply the arguments to invoke this second version of of\n",
     "importance resampling."
    ]
@@ -1774,7 +1774,7 @@
    "source": [
     "Our choice of the `score_high` value of 5. was somewhat arbitrary. To use\n",
     "more informed value, we can make `score_high` into a [*trainable\n",
-    "parameter*](https://www.gen.dev/docs/dev/ref/gfi/#Trainable-parameters-1)\n",
+    "parameter*](https://www.gen.dev/docs/stable/ref/modeling/dml/#trainable_parameters_modeling)\n",
     "of the generative function. Below, we write a new version of the proposal\n",
     "function that makes `score_high` trainable. However, the optimization\n",
     "algorithms we will use for training work best with *unconstrained* parameters\n",
@@ -1928,7 +1928,7 @@
     "Next, we choose type of optimization algorithm we will use for training. Gen\n",
     "supports a set of gradient-based optimization algorithms (see [Optimizing\n",
     "Trainable\n",
-    "Parameters](https://www.gen.dev/docs/dev/ref/parameter_optimization/#Optimizing-Trainable-Parameters-1)).\n",
+    "Parameters](https://www.gen.dev/docs/stable/ref/inference/parameter_optimization/#Parameter-Optimization)).\n",
     "Here we will use gradient descent with a fixed step size of 0.001."
    ]
   },
@@ -1948,7 +1948,7 @@
    "metadata": {},
    "source": [
     "Finally, we use the\n",
-    "[`Gen.train!`](https://www.gen.dev/docs/stable/ref/inference/#Gen.train!)\n",
+    "[`Gen.train!`](https://www.gen.dev/docs/stable/ref/inference/parameter_optimization/#Gen.train!)\n",
     "method to actually do the training.\n",
     "\n",
     "For each epoch, `Gen.train!` makes `epoch_size` calls to the data-generator\n",

tutorials/Data-Driven Proposals in Gen.jl

@@ -390,7 +390,7 @@ measurements = [
 #
 # To see how to use the built-in importance resampling function, run
 # ```?Gen.importance_resampling``` or check out the
-# [documentation](https://www.gen.dev/docs/dev/ref/importance/#Gen.importance_resampling).
+# [documentation](https://www.gen.dev/docs/stable/ref/inference/importance/#Gen.importance_resampling).
 
 # We have provided some starter code.
 
@@ -556,7 +556,7 @@ visualize_inference(measurements, scene_2doors, start, computation_amt=100, samp
 # ## 2. Writing a data-driven proposal as a generative function <a name="custom-proposal"></a>
 
 # The inference algorithm above used a variant of
-# [`Gen.importance_resampling`](https://www.gen.dev/docs/stable/ref/importance/#Gen.importance_resampling)
+# [`Gen.importance_resampling`](https://www.gen.dev/docs/stable/ref/inference/importance/#Gen.importance_sampling)
 # that does not take a custom proposal distribution. It uses the default
 # proposal distribution associated with the generative model. For generative
 # functions defined using the built-in modeling DSL, the default proposal
@@ -614,7 +614,7 @@ num_x_bins = 5
 num_y_bins = 5;
 
 # We will propose the x-coordinate of the destination from a
-# [piecewise_uniform](https://www.gen.dev/docs/dev/ref/distributions/#Gen.piecewise_uniform)
+# [piecewise_uniform](https://www.gen.dev/docs/stable/ref/modeling/distributions/#Gen.piecewise_uniform)
 # distribution, where we set higher probability for certain bins based on the
 # heuristic described above and use a uniform continuous distribution for the
 # coordinate within a bin. The `compute_bin_probs` function below computes the
@@ -676,7 +676,7 @@ compute_bin_probs(num_y_bins, scene.ymin, scene.ymax, measurements[1].y, measure
 end;
 
 # We can propose values of random choices from the proposal function using
-# [`Gen.propose`](https://www.gen.dev/docs/stable/ref/gfi/#Gen.propose).
+# [`Gen.propose`](https://www.gen.dev/docs/stable/ref/core/gfi/#Gen.propose).
 # This method returns the choices, as well as some other information, which we
 # won't need for our purposes. For now, you can think of `Gen.propose` as
 # similar to `Gen.generate` except that it does not produce a full execution
@@ -723,7 +723,7 @@ visualize_custom_destination_proposal(measurements, start, custom_dest_proposal,
 
 # We now use our data-driven proposal within an inference algorithm. There is a
 # second variant of
-# [`Gen.importance_resampling`](https://www.gen.dev/docs/stable/ref/importance/#Gen.importance_resampling)
+# [`Gen.importance_resampling`](https://www.gen.dev/docs/stable/ref/inference/importance/#Gen.importance_resampling)
 # that accepts a generative function representing a custom proposal. This
 # proposal generative function makes traced random choices at the addresses of
 # a subset of the unobserved random choices made by the generative model. In
@@ -742,7 +742,7 @@ visualize_custom_destination_proposal(measurements, start, custom_dest_proposal,
 #
 # This time, use only 5 importance samples (`amt_computation`). You can run
 # `?Gen.importance_resampling` or check out the
-# [documentation](https://www.gen.dev/docs/stable/ref/inference/#Importance-Sampling-1)
+# [documentation](https://www.gen.dev/docs/stable/ref/inference/importance/#Gen.importance_sampling)
 # to understand how to supply the arguments to invoke this second version of of
 # importance resampling.
 
@@ -839,7 +839,7 @@ visualize_inference(measurements, scene, start, computation_amt=5, samples=1000)
 
 # Our choice of the `score_high` value of 5. was somewhat arbitrary. To use
 # more informed value, we can make `score_high` into a [*trainable
-# parameter*](https://www.gen.dev/docs/dev/ref/gfi/#Trainable-parameters-1)
+# parameter*](https://www.gen.dev/docs/stable/ref/modeling/dml/#trainable_parameters_modeling)
 # of the generative function. Below, we write a new version of the proposal
 # function that makes `score_high` trainable. However, the optimization
 # algorithms we will use for training work best with *unconstrained* parameters
@@ -927,13 +927,13 @@ end;
 # Next, we choose type of optimization algorithm we will use for training. Gen
 # supports a set of gradient-based optimization algorithms (see [Optimizing
 # Trainable
-# Parameters](https://www.gen.dev/docs/dev/ref/parameter_optimization/#Optimizing-Trainable-Parameters-1)).
+# Parameters](https://www.gen.dev/docs/stable/ref/inference/parameter_optimization/#Parameter-Optimization)).
 # Here we will use gradient descent with a fixed step size of 0.001.
 
 update = Gen.ParamUpdate(Gen.FixedStepGradientDescent(0.001), custom_dest_proposal_trainable);
 
 # Finally, we use the
-# [`Gen.train!`](https://www.gen.dev/docs/stable/ref/inference/#Gen.train!)
+# [`Gen.train!`](https://www.gen.dev/docs/stable/ref/inference/parameter_optimization/#Gen.train!)
 # method to actually do the training.
 #
 # For each epoch, `Gen.train!` makes `epoch_size` calls to the data-generator